The Internet of Things (IoT) is a network of linked devices that interact with one another to carry out specific functions. Everything from smart home appliances to industrial machinery may be part of this network. The IoT has the potential to revolutionize industries and open up new business opportunities by utilizing the power of big data. As with any new technology, there are substantial obstacles that need to be overcome.

One of the biggest opportunities that big data and the IoT present is the ability to make data-driven decisions in real-time. For example, in the manufacturing industry, sensors on machinery can provide real-time data on performance, allowing for predictive maintenance and reducing downtime. Similarly, in healthcare, IoT devices can monitor patients and provide data to healthcare professionals, allowing for more personalized care.

However, with the amount of data generated by the IoT, there are also significant challenges in terms of managing, processing, and analyzing this data. Traditional data management tools and techniques are often not sufficient to handle the sheer volume of data generated by the IoT. Additionally, there are concerns around data privacy and security, as the IoT often involves sensitive data being transmitted over networks.

Here are few insights from Gartner or Forrester

According to a Gartner report, the combination of big data and the IoT presents significant opportunities for businesses, particularly in areas such as supply chain management, predictive maintenance, and customer engagement. However, the report also highlights the challenges associated with managing and analyzing the large volume of data generated by the IoT, as well as the need for businesses to ensure data security and privacy.

Similarly, a Forrester report emphasizes the potential of the IoT and big data to drive digital transformation in various industries. The report notes that businesses that effectively leverage these technologies can gain a competitive advantage by improving operational efficiency, reducing costs, and delivering better customer experiences. However, the report also warns that businesses must address challenges such as data management and security to realize the full potential of the IoT and big data.

Here are a few challenges and opportunities we should be aware of.

Opportunities:

Real-time data-driven decisions: The ability to collect and analyze real-time data from IoT devices can enable businesses to make data-driven decisions quickly and efficiently.

Increased efficiency and productivity: By using IoT devices to monitor and optimize processes, businesses can increase efficiency and productivity, leading to cost savings and increased revenue.

Improved customer experience: The IoT can be used to collect data on customer behavior and preferences, allowing businesses to offer personalized experiences and improve customer satisfaction.

New revenue streams: The IoT can open up new revenue streams for businesses by enabling them to offer new products and services, such as subscription-based models or pay-per-use models.

Challenges:

Data management: The sheer volume of data generated by IoT devices can be overwhelming for businesses, and traditional data management techniques may not be sufficient to handle it.

Data security and privacy: The IoT involves the transmission of sensitive data over networks, raising concerns around data security and privacy.

Interoperability: As the IoT involves devices from different manufacturers, there can be challenges in ensuring that these devices can communicate and work together seamlessly.

Skill gaps: As the IoT is a relatively new technology, there may be skill gaps in the workforce, making it challenging for businesses to effectively leverage it.

Use Cases:

One use case for big data and the IoT is in the transportation industry. By using IoT devices to collect data on traffic patterns and road conditions, transportation companies can optimize routes and reduce congestion. In agriculture, IoT devices can monitor soil conditions and weather patterns to optimize crop yields. In the energy industry, IoT devices can monitor power usage and detect inefficiencies, leading to cost savings and reduced carbon emissions.

How Indium Software can address

Indium Software has extensive experience in developing and implementing solutions for big data and IoT use cases. For example, our team can develop customized algorithms and machine learning models to analyze IoT data and provide real-time insights. We can also help ensure data privacy and security by implementing robust encryption and access control measures. In addition, our team can develop and deploy custom dashboards and visualizations to make it easy for businesses to understand and act on IoT data.

Here are a few real-time scenarios that illustrate how the combination of big data and the IoT is being used to drive innovation and growth across various industries:

Smart Manufacturing: A manufacturing company has implemented an IoT system to monitor and optimize its production processes in real-time. The system collects data from sensors embedded in manufacturing equipment and uses big data analytics to identify patterns and optimize production. By leveraging this technology, the company has been able to reduce downtime, increase productivity, and improve product quality.

Predictive Maintenance: A transportation company has deployed IoT sensors on its fleet of vehicles to monitor their performance and detect potential maintenance issues before they become major problems. The system collects data on factors such as engine performance, fuel consumption, and tire pressure, and uses big data analytics to identify patterns and predict maintenance needs. By leveraging this technology, the company has been able to reduce maintenance costs, increase vehicle uptime, and improve customer satisfaction.

Smart Agriculture: A farming company has implemented an IoT system to monitor and optimize its crop production processes. The system collects data from sensors embedded in soil and crop fields, as well as weather data and other environmental factors, and uses big data analytics to identify patterns and optimize crop production. By leveraging this technology, the company has been able to increase crop yields, reduce water and fertilizer usage, and improve overall farm productivity.

Wrapping Up

The potential of big data and the IoT is enormous, and businesses that can effectively leverage these technologies will have a significant advantage in the marketplace. However, it is crucial to address the challenges associated with managing and analyzing the data generated by the IoT. Indium Software has the expertise and experience to help businesses overcome these challenges and unlock the full potential of big data and the IoT.

The post Big Data’s Impact on IoT: Opportunities and Challenges in Analytics appeared first on Indium.

Let’s look at a few reasons why modernizing a data warehouse is essential and highlight the benefits it brings.

Traditional data warehouses have served organizations well for many years. These systems typically involve batch processing, where data is extracted from various sources, transformed, and loaded into the warehouse periodically. While this approach has been effective for historical analysis and reporting, it falls short when it comes to real-time decision-making. With the rise of technologies like the Internet of Things (IoT), social media, and streaming data, organizations require access to up-to-the-minute insights to gain a competitive edge.

Why Modernize a Data Warehouse?

Modernizing a data warehouse is crucial for several reasons. First and foremost, it enables organizations to harness the power of real-time data. By integrating data from multiple sources in real-time, businesses can gain immediate visibility into their operations, customer behavior, market trends, and more. This empowers decision-makers to respond quickly to changing circumstances and make data-driven decisions that drive growth and efficiency.

Moreover, modernizing a data warehouse enhances scalability and agility. Traditional data warehouses often struggle to handle the increasing volumes and varieties of data generated today. However, by adopting modern technologies like cloud computing and distributed processing, organizations can scale their data warehousing infrastructure as needed, accommodating growing data volumes seamlessly. This flexibility allows businesses to adapt to evolving data requirements and stay ahead of the competition.

The Need for Modernizing a Data Warehouse

Evolving Business Landscape: The business landscape is experiencing a significant shift, with organizations relying more than ever on real-time insights for strategic decision-making. Modernizing your data warehouse enables you to harness the power of real-time data, empowering stakeholders with up-to-the-minute information and giving your business a competitive edge.

Enhanced Agility and Scalability: Traditional data warehouses often struggle to accommodate the growing volume, velocity, and variety of data. By modernizing, organizations can leverage scalable cloud-based solutions that offer unparalleled flexibility, allowing for the seamless integration of diverse data sources, accommodating fluctuations in demand, and enabling faster time-to-insight.

Accelerated Decision-Making: Making informed decisions swiftly can mean the difference between seizing opportunities and missing them. A modernized data warehouse empowers organizations with real-time analytics capabilities; enabling stakeholders to access and analyze data in near real-time. This empowers them to make quick decisions swiftly, leading to better outcomes and increased operational efficiency.

Benefits of Modernizing a Data Warehouse

Real-Time Decision-Making: Modernizing a data warehouse enables organizations to make timely decisions based on the most up-to-date information. For example, an e-commerce company can leverage real-time data on customer browsing behavior and purchasing patterns to personalize recommendations and optimize marketing campaigns in the moment.

Enhanced Customer Experience: By analyzing real-time data from various touchpoints, organizations can gain deeper insights into customer preferences and behaviors. This knowledge can drive personalized interactions, targeted promotions, and improved customer satisfaction. For instance, a retail chain can use real-time data to optimize inventory levels and ensure products are available when and where customers need them.

Operational Efficiency: Real-time data management allows organizations to monitor key performance indicators (KPIs) and operational metrics in real-time. This enables proactive decision-making, rapid issue identification, and effective resource allocation. For example, a logistics company can leverage real-time data to optimize route planning, reduce delivery times, and minimize fuel consumption.

Wrapping Up

Modernizing a data warehouse is no longer an option but a necessity in today’s data-driven landscape. By adopting real-time data management, organizations can unlock the power of timely insights, enabling faster and more informed decision-making. The benefits extend beyond operational efficiency to include improved customer experience, enhanced competitiveness, and the ability to seize new opportunities as they arise. As technology continues to advance, organizations must prioritize data warehouse modernization to stay agile, remain relevant, and  flourish in a world that is increasingly centered around data.

The post Driving Business Success with Real-Time Data: Modernizing Your Data Warehouse appeared first on Indium.

Strong data management systems are essential in the digital world because data is essential to enterprises. Due to its scalability, flexibility, and usability, Snowflake, a cloud-based data warehouse system, has grown in popularity. However, just like any other system, mistakes can happen and negatively impact corporate operations.

Having a system in place to identify and alert stakeholders is crucial for reducing the effects of errors. Sending error messages to Slack users or channels is one approach to accomplishing this. Slack is a well-liked network for team communication that promotes easy cooperation, making it a great choice for error notification dissemination.

Setting up a Snowflake task to record the issue and a Slack bot to convey the message to the intended recipients is required for sending error notifications from Snowflake to Slack users or channels. Snowflake’s tasks, which allow users to plan and automate data processing workflows, can be used to automate this operation.

Setting up Slack Bot for error notification from Snowflake

The steps for configuring a Slack bot to send out error notifications are as follows:

Step 1: In Slack, create a new bot user.

In Slack, the first step is to establish a new bot user. Visit the Slack API website and log in using your Slack credentials to complete this. After logging in, select “Create a Slack app” from the menu and then follow the on-screen directions to build a new app. Following the creation of the app, you may add a new bot user by selecting “Bot users” from the “Features” part of the app setup page.

Step 2: Create an API token for the bot’s user.

In order to authenticate the bot with the Slack API, we must create an API token for the bot user. To accomplish this, select “Install App” and adhere to the on-screen directions to grant the app access to our Slack workspace. Once the app has been given permission, we can create an API token by selecting “OAuth & Permissions” from the list of options under “Features” on the app settings page. The API token should be copied and saved for further usage. Enable receiving the workspace URL via incoming webhooks as well.

Step 3: Add the bot user to Slack channels.

We can next add the bot user to the Slack channels that will receive error messages from Snowflake after creating the API token. Go to the Slack workplace and find the relevant channels there to achieve this. next look for the bot user we created earlier by selecting the “Add apps” option. Once the bot user has been located, click “Add” to add it to the channel.

Step 4: Configure Snowflake to send error notifications to Slack.

The last step is to set up Snowflake to use the bot user and API token to send error warnings to Slack. Setting up a Snowflake job that records the problem and instructs the Slack bot to send the notification will do this. Depending on the requirements for error notification, the Snowflake job can be configured to execute at a specific frequency, such as every hour or every day.

We must develop a stored procedure that searches the error log table and extracts the error details in order to configure the Snowflake task. The error message can then be sent from the stored procedure to the Slack bot, which will subsequently relay it to the chosen channels, using the Snowflake API. The bot user will be authenticated with the Slack API using the API token previously generated.

Snowflake procedures are multi-language functional, which makes it easier for developers. The procedure is implemented in JavaScript, but it can also be written in Python and Java.

The output shown below illustrates how the JavaScript code was used to access the error log data

To get error information for queries that were executed within the previous 24 hours, this stored procedure runs a query against the TASK_HISTORY table in the INFORMATION_SCHEMA. A JSON object including the query ID, error code, error message, scheduled time, next schedule time, finished time, and duration for each error is returned as the results. Through the connectors, we can ensure that the results are transferred to our desired place as a table, a sheet, or an Excel file.

This saved process can be modified to meet our unique needs for error notification, such as filtering errors based on particular error codes.

Also Read: Unlocking the Power of Data Democratization: Empowering Your Entire Organization with Access to Data

Create a Snowflake task to capture and send notifications to Slack.

Now, using our method and the Slack token we established, we will integrate this error log with Slack to alert the users. This is done by setting up a snowflake task to run every five minutes (this may be altered depending on the requirement and available credits), which will notify Slack of any issues.

To bring the API endpoint and bot token to configure our tasks in the Slack channel and integrate the notification flow, we should construct two important key components in our script. To ensure a stronger grasp on the logs, we also have a number of security measures and constraints that may be applied from both Snowflake’s and Slack’s ends. The task scheduler built into Snowflake, which manages schedule time management and smooth integration, carries out the timetable.

// set up the Slack API endpoint

var slackUrl = ‘<Our Slack bot API endpoint here>’;

// set up the Slack bot token

var slackToken = ‘<Our Slack bot token here>’;

This task, which is scheduled to run every five minutes, invokes a saved function. The stored method searches the QUERY_HISTORY_ERRORS table of the SNOWFLAKE. Use the ACCOUNT_USAGE schema to look for issues that occurred during the last five minutes. If there are problems, it creates a Slack message payload and uses the bot token and endpoint of the Slack API to deliver it to the selected Slack bot. To keep track of the number of errors that have occurred at a particular time or for a specific length of time, the messages include a counter for each error that is encountered. We may check the status of our task by calling it and using,

Show tasks like ‘task_name’ in task_location

This task and stored procedure can be modified to meet our unique error notification needs, such as by altering the error time window or the Slack message content.

Best practises for setting up error notification thresholds and escalation procedures.

Setting up error notification thresholds and escalation processes is crucial for making sure that urgent problems are dealt with and fixed right away. When establishing these procedures, keep the following recommended practises in mind:

1. 1. Establish notification levels: Based on the severity and significance of the issue, establish clear and simple thresholds for error alerts. For instance, we might prefer to be notified of all significant errors, but only if minor errors happen more frequently than a predetermined threshold.
2. 2. Escalation protocols: Establish escalation protocols to guarantee that urgent concerns are handled right away. If problems are not handled within a predetermined amount of time, this may entail notifying management or higher-level support teams.
3. Frequently test our notification processes to make sure that alerts are being sent accurately and that escalation processes are working as intended.
4. 3. Establish a procedure for prioritising and triaging issues in accordance with their seriousness and impact. By doing this, it may be possible to guarantee that urgent problems get attention first and that resources are allocated effectively.
5. 4. Record error alerts: Watch and record error alerts to spot patterns and trends. This can assist in identifying persistent problems and guide future system upgrades.
6. 5. Continually examine and enhance our notification protocols: We must always assess and enhance our notification protocols to make sure they are reliable and effective. This could entail streamlining notification workflows and processes, integrating new technology, or taking customer and support team comments into account.

By adhering to these recommendations, you can make sure that your error notification levels and escalation processes are trustworthy, efficient, and capable of handling urgent situations quickly.

Benefits of using Slack for error notification over email

Slack is a real-time communication platform that enables teams to cooperate and communicate effectively, therefore, it has several advantages over email in terms of alerting users. As a result, notifications are sent immediately and are readily accessible to all team members who have access to the appropriate Slack channel. Email notifications, on the other hand, run the risk of being overlooked, delayed, or lost in a busy inbox, which could have a greater negative impact on business.

Additionally, Slack offers more personalization options for notifications. Users can set up notifications to be sent in several formats, such as text, graphics, and links, which can be customised to fit certain use cases. Teams can better comprehend the failed job with the help of this flexibility, which can be important for troubleshooting and debugging.

Slack can streamline the entire incident management process because it interfaces with a broad variety of third-party applications and services, like Jira and GitHub. For instance, a Slack bot can automatically generate an incident in Jira, assign it to the proper team member, and link it to the relevant chat message when a failed job is identified. The time and effort needed to manage incidents can be greatly reduced because of this connectivity between Slack and other applications, which leads to quicker resolution times and lower operational expenses.

Slack also offers improved process visibility for incident response. Team members can quickly see who is reacting to an incident, what steps are being taken, and when the situation is addressed when notifications are given using Slack channels. This openness encourages responsibility and can assist teams in determining where their incident management procedures need to be strengthened.

The screenshots below show the inability to distinguish a few clear benefits of Slack over email. The first screenshot displays the failure-related email notification, which simply includes the bare minimum of an ID and a description. The user is additionally shown in the second screenshot being triggered and monitoring the member for a longer period of time.

Common error scenarios in Snowflake and how to handle them with Slack notification.

Although Snowflake is a strong data warehousing technology that enables effective data storage and analysis, it can have faults that have an influence on data processing and analysis, just like any complicated system. Following are some typical Snowflake fault scenarios and solutions that utilise Slack notification:

1. 1. Query timeouts: If the query takes too long to run or if there are resource limitations, Snowflake may experience query timeouts. Slack notifications can be used to handle this mistake by notifying users or administrators that the query has timed out and informing them of the solution. We could also set up alerts to let people know when a lengthy query is active.
2. 2. Query failures: Queries might fail for a number of reasons, including incorrect syntax or data issues. Users or administrators can be informed through Slack notice when a query has failed and given instructions on how to fix the problem. To further assist in identifying and resolving the problem, we could also want to provide thorough error messages and logs.
3. 3. Resource limitations: If not enough resources are available to conduct a query, Snowflake may experience resource limitations. Users or administrators can be informed of resource constraints using Slack notifications, and they can be given instructions on how to allocate more resources or improve the query.
4. 4. Data load failures: Snowflake may experience data loading difficulties if the data is incorrectly formatted or has other errors. Users or administrators can be informed through Slack notice that a data load has failed and given instructions on how to fix the problem. To further assist in identifying and resolving the problem, we could also want to provide thorough error messages and logs.
5. 5. Data processing errors: If the data is incorrectly prepared or contains errors, Snowflake may experience data processing difficulties. Users and administrators can be informed of data processing errors and given instructions on how to fix them via Slack notifications. To help with the problem’s diagnosis and resolution, we could additionally want to provide thorough error messages and logs.

Conclusion

It’s crucial to set up error reporting processes if we’re to keep our Snowflake data warehouse reliable and accessible. We can make sure that issues are resolved quickly and that severe errors are escalated to the relevant employees by collecting error information and delivering notifications to Slack channels.

We talked about how to automate the process using Snowflake’s stored procedures and tasks, as well as how to build up a Slack bot to collect error notifications from Snowflake. Defining notification thresholds, utilising various notification channels, and routinely testing notification procedures were some of the best practises we discussed for setting up error notification thresholds and escalation procedures.

By adhering to these best practises, we can build a strong error notification system that minimises downtime while assisting you in swiftly identifying and resolving issues. Setting up issue notifications using Slack may give any data analyst, data engineer, developer, or database administrator access to a potent tool for tracking and maintaining the dependability of your Snowflake data warehouse.

The post Streamline Snowflake Error Logs with Real-time Notifications to Slack Channel appeared first on Indium.

Introduction:

AMQP stands for Advanced Message Queuing Protocol, and it is an open standard application layer protocol. AMQP Message Protocol also deals with publishers and subscribers for the consumer.

One of the key features of AMQP is the message broker, which acts as an intermediary between sender and receiver. The broker receives messages from senders, stores them, and delivers them to their intended recipients based on predefined routing rules. The broker provides a range of features such as message persistence, message acknowledgment, and message prioritisation to ensure reliable and efficient message delivery. 

Several industries, including telecommunications, healthcare, and financial services, use AMQP. It has been widely adopted as a messaging protocol due to its reliability, interoperability, and flexibility.

Now there are four different exchange types:

• Direct Exchange
• Fan Out Exchange
• Topic Exchange and
• Header Exchange

Direct Exchange:

A direct exchange works by matching the routing key, when there is a match, the message is delivered to the queue. Each message sent to a direct exchange must have a routing key. 

If the routing key match, the message can be forwarded to the queue of the message.

For example, suppose there are three nodes named node A, node B, and node C, and a direct exchange named X. If node A is connected to X with a routing key of “key 1”, node B is connected to X with a routing key of “key 2”, and node C is connected to X with a routing key of “key 3”, then when a message is sent to X with a routing key of “key 2”, the message will be routed to node B.

Fan Out Exchange:

A fanout exchange works by sending messages to all of its bound queues. When a message is sent to a fanout exchange, the exchange simply copies it and sends it to all the currently bound queues.

For example, A real-time example of a Fanout Exchange can be a social media platform where a user posts a message that needs to be sent to all the users.

Topic Exchange:

When a message is sent to a topic exchange, the exchange will forward the message to all the queues. If queues have a binding key that matches the routing key, the message is routed to that queue, and finally each customer will receive the message from the queue.

Header Exchange:

A header exchange works by allowing the sender to attach a set of header attributes to each message. The header exchange looks at the headers and compares them to the header values specified in the bindings of each queue. If there is a match between the header of the message and the bindings of a queue, the message is delivered to that queue.       

Also read: Internet of Things in the Automotive Industry Blog.

Advantages of AMQP:

Message orientation, queuing, routing (including publish and subscribe and point-to-point), dependability, and security are the characteristics that set AMQP apart.

It employs techniques to ensure the secure transmission of critical data.

Flexibility:

AMQP includes publisher and subscriber request responses among the many message patterns it supports and point-to-point messaging, which makes it suitable for a variety of business use cases.

These services are provided using AMQP:

Healthcare services:

AMQP can be used to transmit medical data from wearable and implantable devices to healthcare providers, enabling remote monitoring and personalised treatment. It can be used to transmit patient data, test results, and other medical information securely and in real time. By using AMQP, healthcare providers can establish a reliable and secure communication channel to exchange data and messages between different services. The transfer of patient information among various healthcare providers, including hospitals, clinics, and laboratories

Financial services:

AMQP can be used to build reliable and secure messaging systems for financial institutions, including stock exchanges, banks, and trading platforms. It can be used to transmit market data, trade orders, and other financial information securely and efficiently. By using AMQP, financial services providers can improve the speed and efficiency of their communication systems and reduce the risk of delays or errors.

Internet of Things (IoT) services:

the AMQP protocol is designed for reliable, interoperable, and secure communication between different components of distributed applications, including Internet of Things (IoT) devices.

Device-to-cloud communication:

The AMQP protocol enables IoT devices to transmit messages to cloud services for further processing and analysis. For instance, a temperature sensor can utilise AMQP to transmit temperature readings to a cloud-based analytics service.

Overall, AMQP provides a flexible and scalable messaging infrastructure that can support various IoT services, from simple device-to-cloud communication to complex event processing and analytics.

Security:

AMQP provides a range of security features, such as authentication and encryption, to protect messages and prevent unauthorised access.

Conclusion

AMQP is a powerful messaging protocol that enables different applications to communicate with each other reliably, securely, and flexibly. With its client-server architecture and components such as a broker, exchange, queue, producer, and consumer, AMQP provides a robust framework for message-oriented middleware.

The post Seamless Communication: Exploring the Advanced Message Queuing Protocol (AMQP) appeared first on Indium.

Data wrangling can be a time-consuming and labour-intensive process, but it is necessary for achieving reliable and accurate results. In this blog post, we’ll explore various techniques and tools that are commonly used in data wrangling to prepare data for machine learning models.

1. Data integration: Data integration involves combining data from multiple sources to define a unified dataset. This may involve merging data from different databases, cleaning and transforming data from different sources, and removing irrelevant data. The goal of data integration is to create a comprehensive dataset that can be used to train machine learning models.
2. Data visualization : Data visualization is the process of creating visual representations of the data. This may include scatter plots, histograms, and heat maps. The goal of data visualization is to provide insights into the data and identify patterns that can be used to improve machine learning models.
3. Data cleaning: Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in the data. This step includes removing duplicate values, filling in missing values, correcting spelling errors, and removing duplicate rows. The objective of data cleaning is to ensure that the data is accurate, complete, and consistent.
4. Data reduction: Data reduction is the process of reducing the amount of data used in a machine learning model. This may involve removing redundant data, removing irrelevant data, and sampling the data. The goal of data reduction is to reduce the computational requirements of the model and improve its accuracy.
5. Data transformation: Data transformation involves converting the data into a format that is more suitable for analysis. This may include converting categorical data into numerical data, normalizing the data, and scaling the data. The goal of data transformation is to make the data more accessible for machine learning algorithms and to improve the accuracy of the models.        

Also check out this blog on Explainable Artificial Intelligence for a more ethical AI process.

Let’s look into some code:

Here we are taking a student performance dataset with the following features:

1. gender
2. parental level of education
3. math score
4. reading score
5.  writing score

For data visualisation, you can use various tools such as Seaborn, Matplotlib, Grafana, Google Charts, and many others to visualise the data.

Let us demonstrate a simple histogram for a series of data using the NumPy library.

Pandas is a widely-used library for data analysis in Python, and it provides several built-in methods to perform exploratory data analysis on data frames. These methods can be used to gain insights about the data in the data frame. Some of the commonly used methods are:

df.descibe(), df.info(), df.mean() , df.quantile() , df.count()

(- df is pandas dataframe)

Let’s see df.descibe(), This method generates a statistical summary of the numerical columns in the data frame. It provides information such as count, mean, standard deviation, minimum, maximum, and percentile values.

For data cleaning, we can use the fillna() method from Pandas to fill in missing values in a data frame. This method replaces all NaN (Not a Number) values in the data frame with a specified value. We can choose the value to replace the NaN values with, either a single value or a value computed based on the data. 

For Data reduction we can do Sampling, Filtering, Aggregation, Data compression.

In the example below, we are removing the duplicate rows from the pandas drop_duplicates() method.

We will examine data normalisation and aggregation for data transformation; we are scaling the data to ensure that it has a consistent scale across all variables. Typical normalisation methods include z-score scaling and min-max scaling.

    Here, we’re using a StandardScaler to scale the data.  

Use the fillna () method in the Python pandas library to fill in missing or NaN (Not a Number) values in a Data Frame or a Series by using the mean value of the column.

Transform the categorical data in the ‘gender’ column into numerical data using one hot encoding. We will use get_dummies(), a method in the Pandas library of Python used to convert categorical variables into dummy or indicator variables.

In conclusion, data wrangling is an essential step in the machine learning process. It involves cleaning, transforming, and preparing raw data for analysis to ensure that the data used in a machine learning model is accurate, consistent, and error-free. By utilising the techniques and tools discussed in this blog post, data scientists can prepare high-quality data sets that can be used to train accurate and reliable machine learning models.

The post Data Wrangling 101 – A Practical Guide to Data Wrangling appeared first on Indium.

What is Power BI and .pbix files?

Power BI is a market leading business intelligence tool by Microsoft for Cleaning, Modifying and Visualizing raw data to come up with actionable insights. Power BI comes with its own data transformation engine called power query and a formula expression language called DAX (Data Analysis Expressions).

DAX gives power BI the ability to calculate new columns, dynamic measures, and tables inside Power Bi desktop.

By default, Power BI report files are saved with .pbix extension which is a renamed version of a ZIP file which contains multiple components, such as the visuals, report canvas, model metadata, and data.

What is Power BI .pbit file

.pbit is a template file created by Power Bi desktop which is also a renamed version of a ZIP file that contains all the Meta data for the Power BI report but doesn’t contain the data itself. Once we extract .pbit file we get a DataModelSchema file along with other files which contain all the Meta data of a Power BI desktop files.

Later in this blog we will be using these .pbit and DataModelSchema files to extract Power BI desktop Meta data.

What is the Meta data in a Power BI Desktop file

Regarding what you see in the Report View in a Power BI desktop, meta data is everything. You can think of all the information as meta data, including the name, source, expression, data type, calculated tables, calculated columns, calculated measures, relationships and lineage between the model’s various tables, hierarchies, parameters, etc.

We will mainly concentrate on extracting Calculated Measures, Calculated Columns, and Relationships in this blog.

Extraction of Meta data using Python

Python was used to process and extract the JSON from the.pbit file and DataModelSchema. We first converted JSON to a Python dictionary before extracting the necessary Meta data.

Below are the steps we will need to achieve the requirement:

1. Exporting .pbix file as .pbit file

There are two ways to save our power BI desktop file as .pbit file.

• Once we are in Power BI desktop, we have an option to save our file as power BI template(.pbit) file
• We can go to File–>Export–>Power BI Template and save the .pbit file at the desired directory.

2. Unzipping .pbit file to get DataModelSchema file

We can directly unzip the .pbit file using the 7z-Zip file manager or any other file manager. Once we Unzip the file, we will get a folder with the same name as that of the .pbit file. Inside the folder we will get the DataModelSchema file, we will have to change its extension to .txt for reading in python.

3. Reading .pbit and Data model schema file in python

We have an option to directly read the .pbit file in python using the dax_extract library. Second option to read the text file in python and using the JSON module convert it into a Python dictionary. Code can be found in the GitHub repository link given at the end of this file.

4. Extracting Measures from the dictionary

The dictionary that we get consists details of all the tables as separate lists, Individuals tables have details related to the columns and measures belonging to that table, we can loop on each table one by one and get details of columns, Measures etc. Below is an example of the Python code can be found in the GitHub Repository link given at the end of this file.

5. Extracting calculated columns from the Dictionary

Like how we extracted the measures we can loop on each table and get details of all the calculated columns. Below is the sample output of the Python code can be found in the GitHub Repository link given at the end of this file.

Also Read:  Certainty in streaming real-time ETL

6. Extracting relationships from the dictionary

Data for relationships is available in the model key of the data dictionary and can be easily extracted. Below is the sample output of the Python code can be found in the GitHub Repository link given at the end of this file. 

7. Saving Extracted data as an Excel file

All the extracted data can be saved in empty lists and these lists can be used to derive a Pandas data frame. This Pandas data frame can be exported as Excel and easily used for reference and validation purposes in a complex model. Below snapshot gives an idea of how this can be done.

Conclusion

In this blog we learnt about extracting metadata from .pbit and DataModelSchema file. We have created a Python script that allows users to enter the file location of .pbit and DataModelSchema file and then metadata extraction along with excel generation can be automated. The code can be found on the below GitHub also sample excel files can be downloaded from below GitHub link. Hope this is helpful and will see you soon with another interesting topic.

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More than ever, the human race is discovering, revolving, and revolving. The revolution in Artificial Intelligence Domain has brought the whole human species to a new Dawn of personalized services. With more people adapting to the Internet, demands of various services in different phases of life are increasing. Let’s consider the case of Covid Pandemic, the demons are still at war with. In the times of lockdown, to stay motivated we have used Audio Book applications, video broadcasting applications, attended online exercise, Yoga, even Consulted with Doctors through an Application. While the physical streets were closed, there was more traffic online.

All these applications, websites, which we have used, have a simple goal and that is a better service to the user. To do so, they collect personal information directly or indirectly, intentionally or for the sake of betterment. The machines, despite their size starting from laptops to smart watches, even voice assistants are listening to us, watching us every move we made, every word we uttered. Albeit their purpose of doing so is noble, but there’s no guarantee of leakage-proof, intruder-proof and spammers-proof data handling. According to a study by Forbes, on average there are 2.5 quintillion bytes of data generated per day, and this data is increasing year by year exponentially. Data Mining, Data Ingestion and Migration phases are the most vulnerable phases for potential data leakage. The surprising news is the cyber-attacks also happen at a rate of 18 attacks per minute. More than 16 lakh cybercrimes happened in last 3 years in India only.

Need of Data Masking

Besides the online scams and frauds Cyber Attacks, data breaches are major risks to every organization that mines personal data. A data breach is where the attacker gains access to millions to billions of people’s personal information like bank details, mobile numbers, social service numbers, etc. According to the Identity Theft Resource Center (ITRC), 83% of the 1,862 data breaches in 2021 involved sensitive data. These incidents are now considered as equipment of modern warfare.

Data Security Standards

Based on the countries and regulatory authorities there are different rules that need to be imposed to protect sensitive information. European Union States promotes General Data Protection Regulation (GDPR) to protect personal and racial information along with digital information, Health records, biometric and genetic data of individuals. United States Department of Health and Human Service (HHS) passed Health Insurance Portability and Accountability Act that protects and promotes security standards for Privacy of Individually Identifiable Health Information. International Organization for Standardization and the International Electrotechnical Commission’s (IOS/IEC) 27001 and 27018 security standards promote confidentiality, integrity and availability norms for Big Data organizations. In Extract Transform and Load (ETL) services, Data Pipeline services or Data Analytics services sticking to these security norms are crucial and liberating.

Different Security Standards

Read this insightful blog post on– Maximizing AI and ML Performance: A Guide to Effective Data Collection, Storage, and Analysis

Techniques to Protect Sensitive Data

All the security protocols and standards can be summarized into three different techniques: Data De-Identification, Data Encoding and Data Masking. Data De-identification is used to protect sensitive data by removing or obscuring identifiable information. In De-identification technique the original sensitive information will be anonymized i.e., to completely remove those records from database, pseudonymization i.e., to replace the sensitive information with aliases, and lastly the aggregation where data will be grouped and summarized and then will be presented or shared rather than sharing the original elements.

In de-identification the original data format or structure may not be retained. Data Encoding refers to the technique of encoding the data in cyphers which can later be decoded by authorized users. Various encoding techniques are Encryption – key based encryption of data, Hashing – Original data will be converted to hash values using Message Digest (md5), Secure Hash Algorithm (sha1) or BLAKE hashing, etc. In other hand Data masking is the technique of replacing the original data with factious or obfuscated data where the masked data retains the format and structure of original data. All these techniques do not fall into a particular class or follow a hierarchal trend. They are used alone with one another based on the use cases and the cruciality of the data.

Comparative abstraction of major techniques

Data Masking is of two types i.e., Static Data Masking (SDM) and Dynamic Data Masking (DDM). Static Data masking involves replacing sensitive data with realistic but fictitious data with the structure and format of original data. Static Data Masking involves substitution – replacing the sensitive data with fake data, Shuffling – Shuffle the data in a column to manipulate original value and its references, Nulling – Sensitive data will be replaced with Null values. Encryption – encryption of sensitive information, Redaction – partially masking the sensitive data where only one part of the data is visible. Whereas Dynamic Data Masking involves Full masking, partial masking – Mask portion, random masking – mask at random, conditional masking – mask when a specific condition is met, Encoding and Tokenization- convert data to non-sensitive token value that preserves the format and length of original data.

SDM masks data at rest by creating a copy of an existing data set. The copied and masked data can only be used to share in analysis and production teams. Updates to the original data do not reflect in masked data until a new copy is made whereas DDM masks data at query time. The updated data also comes in masked format because of the query. The liveness of data remains intact without worrying about data silos. SDM is the primary choice of data practitioners as it is reliable and completely isolated original data. In other hand, DDM depends on query time masking which poses a chance of failure at some adverse instances.

SDM vs DDM

Data Masking Best Practices

Masking of sensitive data depends on the use case of the resultant masked data. It is always recommended to mask the data in the non-production environment. However, there are some practices that need to be considered for secure and fault-tolerant data masking.

1. Governance: The organization must follow common security practices based on the country it’s operating in and the international data security standards as well.

2. Referential Integrity: Tables with masked data should follow references accordingly for the purpose of join while analyzing the data without revealing sensitive information.

3. Performance and Cost: Tokenization and Hashing often convert the data to a standard size which may be more than actual size. Masked data shouldn’t impact the general query processing time.

4. Scalability: In case of big data the masking technique should be able to mask large dataset and stream data as well.

5. Fault-tolerance: The technique should be tolerant to minimal data ugliness like extra space, comma, special characters etc. By scrutinizing the masking process and resultant data often helps to avoid common pitfalls.

Conclusion

In conclusion, the advancements in technology, particularly in the domain of Artificial Intelligence, have brought about a significant change in the way humans interact with services and each other. The COVID-19 pandemic has further accelerated the adoption of digital technologies as people were forced to stay indoors and seek personalized services online. The increased demand for online services during the pandemic has shown that technology can be leveraged to improve our lives and bring us closer to one another even in times of crisis. As we continue to navigate the post-pandemic world, the revolution in technology will play a significant role in shaping our future and enabling us to live a better life.

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What is Data Management?

Data management is the process of collecting, storing, organizing, maintaining, using, and disposing of data in an efficient and secure manner. It involves establishing policies and procedures for data acquisition, validation, storage, backup and recovery, access, and destruction. Effective data management is crucial to improve operations, and support growth.

Why Should You Adopt a Modern Approach to Data Management?

One of the main forces driving contemporary data management is the growth of big data. Businesses must come up with new methods to store, handle, and analyse data given its exponential increase. With its scalable and adaptable features, cloud computing offers the perfect option.

Increased Business Agility

The processing and analysis of data by organisations has been revolutionised by artificial intelligence (AI) and machine learning (ML). Businesses may use AI and ML to automate complicated data management activities, find patterns and insights, and make decisions more quickly and accurately. Businesses may confidently make data-driven decisions since ML systems can learn from past data to increase their accuracy over time enabling organizations to respond to changing business conditions more effectively..

Improved Data Analytics

Modern data management has increasingly reliant on data analytics and visualisation tools. These tools make it simpler for companies to recognise patterns, trends, and anomalies by transforming raw data into insights that can be used to gain valuable insights from their data.

Data Integration and Interoperability

A modern approach to data management supports the integration of data from multiple sources and systems. The increasing amount of data generated by various sources; data integration has become more challenging. Modern data management solutions must support data integration across multiple platforms and systems, providing a comprehensive view thus enabling organizations to more effectively leverage their data.

Governance with Regulations

Data governance refers to the policies, procedures, and controls used to manage data quality, security, and privacy. With the increasing importance of data in business decision-making, data governance has become critical to ensure data accuracy, security, and compliance with regulatory requirements. A modern approach to data management helps organizations comply with data privacy regulations, such as GDPR and HIPAA, and reduces the risk of penalties for non-compliance.

Also read: Crucial Role that Data Fabric Plays in Data Management Today

What should be considered for a successful approach to modern data management?

Having a successful approach to modern data management requires several key elements, including:

Effective data governance aids for data management to be successful, it is essential to implement clear policies and procedures for data gathering, validation, storage, backup, recovery, access, and deletion. Making sure that data is always correct, comprehensive, and consistent is essential for making well-informed decisions and running a successful organisation, so it is important to have a data quality management system in place.

The key to ensuring that data management operations are successful, efficient, and complement the overall business plan is having devoted, well-trained data management staff. To meet the organization’s changing demands, data management should be a process that is continually evaluated and improved.

Data management can present several challenges, including:

Data Volume: The exponential growth of data, also known as big data, can present a challenge for organizations that need to store, process, and analyze large amounts of data.

Data Variety: The increasing variety of data types and formats can make it difficult for organizations to manage and integrate data from multiple sources.

Data Quality: Ensuring the accuracy, completeness, and consistency of data can be challenging, especially as data is sourced from multiple systems and platforms.

Data Security: Protecting sensitive data from unauthorized access and ensuring compliance with data privacy regulations, such as GDPR and HIPAA, can be challenging.

Data Integration: Integrating data from multiple sources and systems can be difficult, especially when data formats are incompatible or data is stored in silos.

Data Governance: Implementing effective data governance policies and procedures that ensure consistent and compliant data management practices can be challenging.

Data Management Teams: Finding and retaining skilled data management professionals can be difficult, especially as the demand for these skills continues to grow.

Budget and Resources: Securing sufficient budget and resources to implement and maintain a robust data management program can be a challenge.

These challenges highlight the importance of adopting a comprehensive, well-planned, and well-executed data management strategy that takes into account the unique needs and requirements of each organization.

How Do You Create a Modern Data Architecture?

To create modern data architecture, you can follow these steps:

Step 1:  Start by defining the business requirements and determining what information the organisation needs to gather, store, and analyse in order to achieve its objectives. List the numerous data sources, including social media, transactional systems, logs, and third-party APIs. decide what modifications are required to accommodate the new design, and evaluate the existing data infrastructure.

Step 2:  Choose the best technology for data storage, processing, and analysis based on your needs and the sources of your data. Data lakes, data warehouses, and cloud services may all fall under this category. Then Design the data architecture in accordance with the needs, data sources, and technologies you have chosen. Creating data models, data pipelines, and data access patterns may be part of this. finally Implement the data architecture, then test it to see if it functions as expected and satisfies the criteria.

Step 3: Maintain regular monitoring of the data architecture and make changes as necessary to make sure it continues to fulfil the demands of the enterprise. Keep in mind that a modern data architecture needs to be scalable, versatile, and secure to suit the business’s continuously changing needs.

Conclusion

By leveraging the latest technologies and tools, and by having dedicated and well-trained data management teams in place, organizations can ensure that they have the right data at the right time to support their business needs. By adopting a modern approach to data management, organizations can increase operational efficiency, improve customer understanding, and gain a competitive advantage in their respective markets.

In conclusion, modern data management is essential for business success as it enables organizations to effectively collect, store, and analyze data to support informed decision making and drive business growth. The volume, variety, and velocity of data continues to increase, making it more important than ever for organizations to adopt modern data management practices that support effective data governance, security, and privacy.

The post Why Modern Data Management is Essential for Business Success appeared first on Indium.

Small data consumes less data but is just as insightful because it leverages techniques such as;

• Time-series analysis techniques
• Few-shot learning
• Synthetic data
• Self-supervised learning

Wide refers to the use of unstructured and structured data sources to draw insights. Together, small and wide data can be used across industries for predicting consumer behavior, improving customer service, and extracting behavioral and emotional intelligence in real-time. This facilitates hyper-personalization and provides customers with an improved customer experience. It can also be used to improve security, detect fraud, and develop adaptive autonomous systems such as robots that use machine learning algorithms to continuously improve performance.

Why is big data not relevant anymore?

First being the large volumes of data being produced everyday from nearly 4.9 billion people browsing the internet for an average of seven hours a day. Further, embedded sensors are also continuously generating stream data throughout the day, making big data even bigger.

Secondly, big data processing tools are unable to keep pace and pull data on demand. Big data can be complex and difficult to manage due to the various intricacies involved, right from ingesting the raw data to making it ready for analytics. Despite storing millions or even billions of records, it may still not be big data unless it is usable and of good quality. Moreover, for data to be truly meaningful in providing a holistic view, it will have to be aggregated from different sources, and be in structured and unstructured formats. Proper organization of data is essential to keep it stable and access it when needed. This can be difficult in the case of big data.

Thirdly, there is a dearth of skilled big data technology experts. Analyzing big data requires data scientists to clean and organize the data stored in data lakes and warehouses before integrating and running analytics pipelines. The quality of insights is determined by the size of the IT infrastructure, which, in turn, is restricted by the investment capabilities of the enterprises.

What is small data?

Small data can be understood as structured or unstructured data collected over a period of time in key functional areas. Small data is less than a terabyte in size. It includes;

• Sales information
• Operational performance data
• Purchasing data

It is decentralized and can fit data packets securely and with interoperable wrappers. It can facilitate the development of effective AI models, provide meaningful insights, and help capture trends. Prior to adding larger and more semi-or unstructured data, the integrity, accessibility, and usefulness of the core data should be ascertained.

Benefits of Small Data

Having a separate small data initiative can prove beneficial for the enterprise in many ways. It can address core strategic problems about the business and improve the application of big data and advanced analytics. Business leaders can gain insights even in the absence of substantial big data. Managing small data efficiently can improve overall data management.

Some of the advantages of small data are:

• It is present everywhere: Anybody with a smartphone or a computer can generate small data every time they use social media or an app. Social media is a mine of information on buyer preferences and decisions.
• Gain quick insights:  Small data is easy to understand and can provide quick actionable insights for making strategic decisions to remain competitive and innovative.
• It is end-user focused: When choosing the cheapest ticket or the best deals, customers are actually using small data. So, small data can help businesses understand what their customers are looking for and customize their solutions accordingly.
• Enable self-service: Small data can be used by business users and other stakeholders without needing expert interpretation. This can accelerate the speed of decision making for timely response to events in real-time.

For small data to be useful, it has to be verifiable and have integrity. It must be self-describing and interoperable.

Indium can help small data work for you

Indium Software, a cutting-edge software development firm, has a team of dedicated data scientists who can help with data management, both small and big. Recognized by ISG as a strong contender for data science, data engineering, and data lifecycle management services, the company works closely with customers to identify their business needs and organize data for optimum results.

Indium can design the data architecture to meet customers’ small and large data needs. They also work with a variety of tools and technologies based on the cost and needs of customers. Their vast experience and deep expertise in open source and commercial tools enable them to help customers meet their unique data engineering and analytics goals.

FAQs

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Indeed, data is the most valuable, enduring asset of any organization, providing the foundation for digital transformation and strategy.

Effective data management is an essential part of today’s unpredictable business environment. Managing and understanding data better can help companies make informed and profitable business decisions.

The total volume of data that organizations across the world create, capture, and consume is forecast to reach 59 zettabytes in 2021, according to Statista. This data does not only comprise structured data in the form of documents, PDFs, and spreadsheets, it also includes tweets, videos, blog articles and more that make up unstructured data, which is essentially eclipsing the volume of structured data. Therefore, organizations not only face storage challenges but have a significant challenge in processing the wide-ranging data types.

Data Modernization

The process of migrating siloed data to modern cloud-based databases or lakes from legacy databases is known as data modernization. It enables organizations to be agile and eliminate bottlenecks, inefficiencies, and complexities of legacy systems.

A modernized data platform helps in efficient data migration, faster ingestion, self-service discovery, near real-time analytics and more key benefits.

For any modern business focused on building and updating the data architecture to spruce up their data core, data modernization is not only important but essential.

To gain optimal value, accelerate operations and minimize capital expenditure, companies must build and manage a modern, scalable data platform. Equally, it is vital to identify and deploy frameworks of data solutions along with data governance and privacy methodologies.

Data modernization is not without challenges as it requires creating a strategy and robust methods to access, integrate, clean, store, and prepare data.

Tips For Successful Data Modernization

Data modernization is critical for any modern business to stay ahead of the curve. With that said, let us find out how companies can be successful in their data modernization efforts.

Revise Current Data Management Strategy And Architecture

It is important to have an in-depth understanding of the organization’s business goals, data requirements and data analytics objectives when a company starts modernizing.

Thereafter, a data management architecture can be designed to integrate existing data management systems and tools, while innovative methods and models can be leveraged to accomplish the organization’s immediate objectives and adapt to future needs.

A well-designed architecture will enable data modernization to be approached systematically and holistically, thereby eliminating data silos and compatibility issues. It will also deliver consistent value and be flexible to integrate new capabilities and enhancements.

Inventory And Mapping Of Data Assets

If an organization cannot identify where the data assets are and what is protecting them, it will be tough to know if the access provided is suitably limited or widely available to the internet.

It is essential for organizations to first understand what data is being collected, what is being collected and what is being sent out. This helps identify the requirements and how a modern data management technology can help simplify the company’s data and analytics operating model.

The best way to begin a meaningful transformation is to simplify the problem statement. Hybrid cloud is also an integral part of any modern data management strategy.

Data Democratization A Core Objective

Until a few years ago, organizations had one major reason to modernize their data management ecosystems—which was to manage their rapidly growing data volumes.

Today the single, overriding reason is data democratization, which is about getting the right data at the right time to the right people.

It gives organizations wide-ranging abilities such as implementing self-service analytics, deploying large data science and data engineering teams, building data exchanges and zones for collaboration with trading partners and go after more data management activities.

Another key advantage of democratizing data is it helps companies achieve data trust and affords them more freedom to concentrate on transformative business outcomes and business value.

Robust governance is another focus area for organizations, who can thereby reduce data preparation time and give data scientists and other business issues the time to focus on analysis.

Technology Investment

Continuous investment in master governance and data management technologies is the best way to gain maximum control over organizational data.

Assuming ownership of data elements and processes, with leadership support, can often be ignored in data management programs but they are a key enabler in managing complex environments.

It is important for chief information officers (CIOs) to take stock of the legacy technologies present on-premises, the decision support system that is ageing and will be out of contract in a few months and more contribute to data modernization projects being successful.

Data Accountability

Establishing data accountability is a basic yet crucial step in reimagining data governance. Organizations that go beyond process and policy and prioritize insights and quality measures tend to be the most successful when it comes to data modernization.

In today’s rapidly changing world, almost everything is connected and digital. In this scenario, every bit of data about customers, transactions and internal processes are business assets that can be mined to enhance customer experience and improve the product.

Among the key issues facing IT leaders is while digital points continue to increase rapidly, many remain locked to monolithic legacy systems. A holistic look at solution development and delivery that leverage Agile, DevOps, Cloud and more such approaches are essential.

Summary

It is important for organizations to be aware of the evolving data management methods and practices. It could be said that data management is one of the most demanding issues IT leaders are likely to encounter in the year 2021 and beyond. For a company’s data modernization process to be successful, their data management approach should align with their overall business strategy.

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