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.

Often, the data team needs different solutions to process unstructured data, enable business intelligence, and build machine learning models. But with the unified Databricks Lakehouse Platform, all these are unified. It also simplifies data processing, analysis, storage, governance, and serving, enabling data engineers, analysts, and data scientists to collaborate effectively.

For the cloud engineer, this is good news. Managing permissions, networking, and security becomes easier as they only have one platform to manage and monitor the security groups and identity and access management (IAM) permissions.

Challenges Faced by Cloud Engineers

Access to data, reliability, and quality, are key for businesses to be able to leverage the data and make instant and informed decisions. Often, though, businesses face the challenge of:

• No ACID transactions: As a result, updates, appends, and reads cannot be mixed
• No Schema Enforcement: Leads to data inconsistency and low quality.
• Integration with Data Catalog Not Possible: Absence of single source of truth and dark data.

Since object storage is used by data lakes, data is stored in immutable files that can lead to:

• Poor Partitioning: Ineffective partitioning leads to long development hours for improving read/write performance and the possibility of human errors.

• Challenges to Appending Data: As transactions are not supported, new data can be appended only by adding small files, which can lead to poor quality of query performance.

Databricks Advantages

Databricks helps overcome these problems with Delta Lake and Photon.

Delta Lake: A file-based, open-source storage format that runs on top of existing data lakes, it is compatible with Apache Spark and other processing engines and facilitates ACID transactions and handling of scalable metadata, unifying streaming and batch processing.

Delta Tables, based on Apache Parquet, is used by many organizations and is therefore interchangeable with other Parquet tables. Semi-structured and unstructured data can also be processed by Delta Tables, which makes data management easy by allowing versioning, reliability, time travel, and metadata management.

It ensures:

• ACID
• Handling of scalable data and metadata
• Audit history and time travel
• Enforcement and evolution of schema
• Supporting deletes, updates, and merges
• Unification of streaming and batch

Photon: The lakehouse paradigm is becoming de facto but creating the challenge of the underlying query execution engine unable to access and process structured and unstructured data. What is needed is an execution engine that has the performance of a data warehouse and is scalable like the data lakes.

Photon, the next-generation query engine on the Databricks Lakehouse Platform, fills this need. As it is compatible with Spark APIs, it provides a generic execution framework enabling efficient data processing. It lowers infrastructure costs while accelerating all use cases, including data ingestion, ETL, streaming, data science, and interactive queries. As it does not need code change or lock-in, just turn it on to get started.

Read more on how Indium can help you: Building Reliable Data Pipelines Using DataBricks’ Delta Live Tables

Databricks Architecture

The Databricks architecture facilitates cross-functional teams to collaborate securely by offering two main components: the control plane and the data plane. As a result, the data teams can run their processes on the data plane without worrying about the backend services, which are managed by the control plane component.

The control plane consists of backend services such as notebook commands and workspace-related configurations. These are encrypted at rest. The compute resources for notebooks, jobs, and classic SQL data warehouses reside on the data plane and are activated within the cloud environment.

For the cloud engineer, this architecture provides the following benefits:

Eliminate Data Silos

A unified approach eliminates the data silos and simplifies the modern data stack for a variety of uses. Being built on open source and open standards, it is flexible. Enabling a unified approach to data management, security, and governance improves efficiency and faster innovation.

Easy Adoption for A Variety of Use Cases

The only limit to using the Databricks architecture for different requirements of the team is whether the cluster in the private subnet has permission to access the destination. One way to enable it is using VPC peering between the VPCs or potentially using a transit gateway between the accounts.

Flexible Deployment

Databricks workspace deployment typically comes with two parts:

– The mandatory AWS resources

– The API that enables registering those resources in the control plane of Databricks

This empowers the cloud engineering team to deploy the AWS resources in a manner best suited to the business goals of the organization. The APIs facilitate access to the resources as needed.

Cloud Monitoring

The Databricks architecture also enables the extensive monitoring of the cloud resources. This helps cloud engineers track spending and network traffic from EC2 instances, register wrong API calls, monitor cloud performance, and maintain the integrity of the cloud environment. It also allows the use of popular tools such as Datadog and Amazon Cloudwatch for data monitoring.

Best Practices for Improved Databricks Management

Cloud engineers must plan the workspace layout well to optimize the use of the Lakehouse and enable scalability and manageability. Some of the best practices to improve performance include:

• Minimizing the number of top-level accounts and creating a workspace as needed to be compliant, enable isolation, or due to geographical constraints.
• The isolation strategy should ensure flexibility without being complex.
• Automate the cloud processes.
• Improve governance by creating a COE team.

Indium Software, a leading software solutions provider, can facilitate the implementation and management of Databricks Architecture in your organization based on your unique business needs. Our team has experience and expertise in Databricks technology as well as industry experience to customize solutions based on industry best practices.

FAQ

The post What Cloud Engineers Need to Know about Databricks Architecture and Workflows appeared first on Indium.

Given the volume of data being generated, it also comes as no surprise that the global big data and data engineering services market size is expected to grow from $39.50 billion in 2020 to $87.37 billion by 2025 at a CAGR of 17.6%.

While the availability of large volumes of unstructured data is driving this market, it is also being limited by a lack of access to data in real time. What businesses need is speed to make the best use of data at scale.

Mozart’s Modern Data Platform for Speed and Scale

One of the biggest challenges businesses face today is that each team or function has different software that is built specifically for the purpose. As a result, data is scattered and siloed, making it difficult to get a holistic view. Businesses need a data warehouse solutions to unify all the data from different sources to derive value. This requires transformation of data into a format that can be used for analytics. Often, businesses use homegrown solutions that can add to time and delays, not to mention costs.

Mozart Data is a modern data platform that enables businesses to unify data from different sources within an hour, to provide a single source of truth. Mozart Data’s managed data pipelines, data warehousing, and transformation automation solutions enable the centralization, organization, and analysis of data, proving to be 70% more efficient than traditional approaches. The modern scalable data stack comes with all the required components, including a Snowflake data warehouse.

Some of its key functions include;

• Deduplication of reports
• Unification of conventions
• Making suitable changes to data, enabling BI downstream

This empowers business users with access to accurate, clean, unified, and uniform data needed for generating reports and analytics. Users can schedule  data transformation automation in advance too. Being scalable, Mozart enables incremental transformation for processing large volumes of data quickly, at lower costs. This also helps business users and data scientists focus on data analysis, than on data wrangling.

Benefits of Mozart Data Platform

Some of the features of Mozart Modern Data Platform, that enable data transformation at scale, include:

Fast Synchronization

Mozart Data Platform allows no-code integration of data sources for faster and reliable access.

Integrate Data to Answer Complex Questions

By integrating data from different databases and third-party tools, Mozart helps business users make decisions quickly and respond in a timely manner, even as the business and data grow.

Synchronize with Google Sheets

It enables users to collaborate with others and operationalize data in a tool they’re most comfortable using: Google Sheets. It allows data to be synchronized with Google Sheets or enables a one-off manual export.

Use Cases of the Mozart Data Platform

Mozart Data Platform is suitable for all kinds of industries, businesses of any size, and for a variety of applications. Some of these include:

Marketing

Mozart enables data-driven marketing by providing insights and answers to queries faster. It creates personalized promotions and increases ROI by segmenting users, tracking campaign KPIs, and identifying appropriate channels for the campaigns.

Operations

It improves strategic decision-making, backed by data with self-service. It also automates tracking and monitoring of key business metrics. It slices and dices data from all sources and presents a holistic view of the same by predicting trends, expenses, revenues and costs.

Finance

It helpsplan expenses and incomes, track expenditure, and automate financial reporting. Finance professionals can access data without depending on the IT team and automate processes to reduce human error.

Revenue Operations

It improves revenue-generation through innovation and identifies opportunities for growth with greater visibility into all functions. It also empowers different departments with data to track performance, and allocate budgets accordingly.

Data Engineers

It encourages data engineers to build data stacks quickly and not worry about maintenance.It provides end-users with clean data for generating reports and analytics.

Indium to Build Mozart Data Platform at Scale for Your Organization

Indium Software is a cutting edge data solution provider that empowers businesses with access to data that help them break barriers to innovation and accelerate growth. Our team of data engineers, data scientists, and analysts combine technical expertise with experience to understand the unique needs of our customers and provide solutions best suited to achieve their business goals.

We are recognized by ISG as a Strong Contender for Data Science, Data Engineering, and Data Lifecycle Management Services. Our range of services include Application Engineering, Data and Analytics, Cloud Engineering, Data Assurance, and Low Code Development. Our cross-domain experiences provide us with insights into how different industries function and the data needs of the businesses operating in that environment.

FAQs

The post <strong>Mozart Data’s Modern Data Platform to Extract-Centralize-Organize-Analyze Data at Scale</strong> 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

The post Big data: What Seemed Like Big Data a Couple of Years Back is Now Small Data! appeared first on Indium.

Data Virtualization service provides a unified view of all data, structured and unstructured, without replicating or storing it. It enables centralized security and governance with data having to be moved physically. It uses pointers to direct users to the blocks requiring a smaller storage footprint, improving the speed of access to stored data in real-time. Businesses can run predictive, visual, and streaming analytics on real-time data relevant to their needs.

Benefits of Data Virtualization

Data virtualization improves the agility of businesses to face the increasingly competitive and fast-changing environment. While they have access to large volumes of internal and external data, traditional Extract Transform Load (ETL) systems or data warehouse approaches are insufficient to meet the data management needs of companies. With data virtualization, business users can access and consume production-quality data without the intervention of database administrators. This provides them with access to data in real-time to be able to respond quickly, make informed decisions, and improve productivity.

Some of the benefits of data virtualization include:

● Derive value from data

● Take faster decisions

● Improve customer satisfaction

● Reduce risks

● Accelerate speed of solution development

● Enhance productivity

● Scale-up quickly

However, some underlying criteria must be met to ensure that data virtualization helps businesses meet their goals.

You might be interested in : Data Virtualization and its Basics

5 Critical Factors to Get Data Virtualization Right

Data virtualization promises much but also requires the following five key factors to be
fulfilled to deliver on its promise. These include:

1. Setting Data Virtualization Goals: Understanding the business need for setting
up data virtualization is a critical step. This will impact the design of the data
virtualization architecture, identify the relevant data sources, and create a
reusable view.

2. Set Performance Benchmarks: To assess the effectiveness of the data
virtualization endeavor, it is important to establish performance benchmarks.
This will help measure performance and monitor progress, enabling mid-course
correction where needed.

3. Define Scope and Set Boundaries: Limit the scope of the pilot to a few sources
or sets of views and align them clearly to the objectives and taking cognizance of
the complexity of the end target.

4. Ready the Pilot: Have clear-cut source connections, develop the base and
derived view, facilitate access to sufficient cache, publish services, and measure
against the KPIs.

5. Document: Documenting the key observations and learnings is essential as it can
guide the implementation for greater success.

Denodo Advantage

Denodo is a leader in the data virtualization market as it provides unique capabilities
that can transform a business to become data-driven. Some of the key features of
Denodo data virtualization platform include:

● Dynamic query optimizer

● Advanced caching

● Self-Service Data Discovery

Business users can search for any data or meta data in a self-service manner without
depending on the IT team. This speeds up analytics and delivers faster results. It
integrates data from cloud and on-prem systems to provide a single source of truth for
reporting, operational, and analytical needs of the business users without being
impacted by the underlying complexity.
This results in benefits such as:

● 30% reduction in resources

● 50-70% savings compared to traditional approaches

● 10x faster data delivery

● 300% increase in end-user productivity

Indium Approach

Indium Software is a Denodo partner with expertise and experience in data
management, data virtualization, and Denodo platform. Our comprehensive range of
Denodo services can help your organization leverage data for several use cases across
functions.

We evaluate the source data for its relevance to the organizational goals, set metrics for
ROI and performance, assess the intended use of data and its complexity, and evaluate
the challenges to the project.

Rapid prototyping and feasibility testing are 50% faster than the traditional approach on
the Indium pilot framework. We also meet industry standards, comply with regulations,
and deliver on promise.
Our Denodo capabilities include:

● Data Modelling with Virtual Sandbox: Providing a single view of data from multiple sources in real-time view without moving the data into a new repository

● Self Service BI: Empowering business users with secure access to data using a virtualized layer

● Enterprise Business Data Glossary: Harmonizing and cataloging data in the virtual layer independent of the named convention in the source system for downstream applications

● Unified Data Governance: Allowing downstream applications access to data without a trace of the physical location or the source of the data

● Virtual MDM: Using the virtual layer as the master data to build master data harmonization

●Enterprise Data Services: Providing downstream applications with data access using virtualized data as the central gateway

Our team of experts work closely with our customers to understand their objectives and
design the architecture to maximize benefits.

The post Data Virtualization 101: 5 Key Factors to Getting it Right  appeared first on Indium.

Data is a platform through which businesses gain a competitive advantage and succeed and thrive, but to meet customer and business needs, it is imperative that data is delivered quickly (in near-real-time). With the prevalence of Internet of Things (IoT), smartphones and cloud, the volume of data is incredibly high and continues to rise; types and sources of data are aplenty too, making data management more challenging than ever.

Companies today have their data in multiple on-premise sites and public/private clouds as they move into a hybrid environment. Data is structured and unstructured and is held in different formats (relational databases, SaaS applications, file systems, data lakes, data stores, to name a few). Further, myriad technologies—changed data capture (CDC), real-time streaming, batch ETL or ELT processing, to name a few—are required to process the data. With more than 70 percent of companies leveraging data integration tools, they find it challenging to quickly ingest, integrate, analyze, and share the data.

As a consequence, data professionals, an IDC study finds, spend 75% of the time on tasks other than data analysis, hampering companies from gaining maximum value from their data in timely fashion.

What is the Solution?

Data fabric is one way for organizations to manage the collection, integration, governance and sharing of data.

A common question is: What is a data fabric?

It is a distributed data management platform with the main objective of combining data access, storage, preparation, security, and analytics tools in a compliant way to ensure data management tasks are easier and efficient. The data fabric stack includes the data collection and storage layer, data services layer, transformation layer and analytics layer.

Following are some of the key benefits of data fabric:

• Provides greater scalability to adapt to rising data volumes, data sources, et cetera
• Offers built-in data quality, data governance and data preparation capabilities
• Offers data ingestion and data integration
• Supports Big Data use cases
• Enables data sharing with internal and external stakeholders through API support

It used to be that organizations wanted all their data in a single data warehouse, but data has become increasingly distributed. Data fabric is purposely created to address the siloed data, enabling easy access and integration of data.

The Capabilities of a Data Fabric Solution

It is essential that a data fabric has the following attributes for enterprises to gain the maximum value from their data.

Full visibility: Companies must be able to measure the responsiveness of data, data availability, data reliability and the risks associated with it in a unified workspace

Data semantics: Data fabric should enable consumers of data to define business value and identify the single source of truth irrespective of structure, deployment platform and database technology for consistent analytics experience

Zero data movement: Intelligent data virtualization provides a logical data layer for representation of data from multiple, varied sources without the need to copy or transfer data

Platform and application-agnostic: Data fabric must be able to quickly integrate with a data platform or business intelligence (BI)/machine learning application as per the choice of data consumers and managers alike

Data engineering: Data fabric should be able to identify scenarios and have the speed of thought to anticipate and adapt to a data consumer’s needs, while reducing the complexities associated with data management

Data Fabric – the key to next-gen Data Management

Data fabrics have emerged as the need of the hour as the support for operational data management and integration becomes complex for databases.

In fact, data fabric is the layer which supports key business applications, particularly those running artificial intelligence (AI) and machine learning (ML) workloads. It means, for organizations that aim to reap the benefits of implementing AI, leveraging a data fabric will help accelerate the ability to adopt AI products.

Digital transformation leads the strategic agenda for most companies and IT leaders. Data is a critical part of a successful digital transformation journey as it helps create new business propositions, enable new customer touchpoints, optimize operates and more. Data fabric is the enabler for organizations to achieve these with its advanced data integration and analytical capabilities, and by providing connectors for hybrid systems.

As organizations aim to stay updated on emerging technologies and trends to gain a competitive edge, the demand for data fabric will only get stronger.

The post Why Data Fabric is the key to next-gen Data Management? appeared first on Indium.

However, as a Gartner analysis points out, all that data being generated can be of any use only if the right data is provided to the right people at the right time. Today, in the world of digital transformation, it means ‘Right Now!’ Businesses need information as it is unraveling and not in some distant future. They need to take instantaneous decisions, respond to customer queries and needs, solve supply chain problems, handle logistics issues as they are happening. Any delay can mean missed opportunities, costing the business in millions. It can impact revenues and growth prospects.

This instant need for insights requires data management to keep pace with the changing requirements and manage data in innovative ways to meet the real-time data needs. The role of data engineering is becoming even more critical now and the processes and tools are undergoing a change to provide clean, trustworthy and quality to business users across the enterprise to make informed decisions at the speed of light.

Evolving Role of Data Engineering

In every organization, data flows through multiple sources and in multiple formats. The data is stored in different databases, creating silos. Data access becomes a challenge, hiding vital information from the decision-makers that could change the course of their business. Moreover, data needs to be cleaned, transformed, processed, summarized, enriched and stored securely, all as it is flowing into the organization.

The role of data engineering is now expanding. Data engineers do all that they were doing earlier to provide data to data analysts, scientists and business leaders. But they also need to be able to match the pace of the requirements of these users. It is no more about creating metadata in a leisurely way but creating data pipelines right from acquisition to encoding, instantly for current and future needs.

Real time creation of data pipeline requires the following four steps:

• Capture – Collect and aggregate streams (Using Flume)
• Transfer – Using Kafka for real-time and Flume for batch – Flume
• Process – Real-time data is processed using Spark and batch processing is performed on Hadoop using Pentaho
• Visualize – Visualization of both real-time and batch processed data

Meeting Real-Time Needs

As businesses become future-ready, the approach to data engineering is also undergoing a transformation. The function is fast-changing where batch ETL is being replaced by database streaming, with traditional ETL functions occurring in real-time. The connection between data sources and the data warehouse is strengthening and with smart tools self-service analytics becoming the need of the hour. Data science functions are also getting automated to be able to predict future trends quickly and course correct current strategies to meet those needs.

Another trend that is emerging is that of hybrid data architectures where on-premise and cloud environments are co-existing, with data engineering now having to deal with data from both these sources.

Data-as-it-is is another trend that is changing the way storing of data is being impacted – is becoming nearly irrelevant due to the growing popularity of real-time data processing. While this has made data access simpler, data processing has become more difficult.

All these trends have expanded the role of the data engineer. However, where are the data engineers to meet this demand?

A Databridge report suggests that though Big Data is exciting and suggests many possibilities for businesses, in reality, lack of a skilled workforce and complexity in insights extraction are major hurdles to it being leveraged and its potential explored to the optimum. Since 2012, the job postings for data engineers have gone up 400%, and in the last one year, they’ve almost doubled.

Especially in the last two years, there is more digital transformation of businesses because of which there has been a tremendous increase in the data being generated. This is only going to grow as more businesses opt for digital transformation and experience an explosion in data in their organizations.

Indium as Data Engineering Partner

Businesses will need partners with experience in Big Data and Data engineering to be able to handle their data processing in real-time while keeping their costs low.

A partner such as Indium Software, with more than two decades of experience in cutting edge technologies, can be an ideal fit. Our team has expertise in data engineering, handling data processing in real-time and the latest technologies such as Python, SQL, NoSQL, MapReduce, HIVE, PIG, Apache Spark, Kafka.

Indium offers Big Data technology expertise with rich delivery experience to enable our clients to leverage Big Data and business analysis even on traditional platforms such as enterprise data warehouse, BI, etc.

A well-thought-out reference architecture for Big Data solutions that is flexible, scalable and robust and using standard frameworks for executing these services, Indium also helps organizations improve efficiencies, reduce TCO and lower risk with commercial solutions. Indiums offers consulting, implementation, on-going maintenance and managed services to derive actionable insights and make quicker and informed decisions.

A leading, 130-year-old Italian bank with more than 300 branches spread across Italy,

Ireland, India and Romania, offering a wide range of customized financial and

banking products wanted a scalable real-time solution to analyze data from all

workloads and provide operational intelligence, to proactively reduce server downtime. They wanted their server logs mined in real-time for faster troubleshooting, RCA and to prevent server performance issues.

Indium used Apache Flume daemons to fetch and push server logs to Apache Storm through Kafka messaging queue. The data processing was done in real-time in Apache Storm and the

processed data was loaded into Hbase NoSQL database. D3.js visualization was built by the bank on top of this processed data. The raw data from Apache Storm was pushed to Apache SOLR to enable Admins perform text searches and gain insights, also in real-time.

The entire application was built from the ground up in less than two weeks and tested with production grade data and tuned for performance to get real-time insights from server logs generated in 420 servers.

Indium can help you get future-ready and make informed decisions based on real-time data. Contact us now to find out how.

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