The following subjects will be covered in the blog:

• What is Google Big Query and Looker Studio?
• How can I real-time connect Big Query data with Looker Studio?
• The advantages of Looker Studio with Big Query.
• An explanation of how using Looker Studio with a Big Query client saves a tonne of time and effort.

Organisations can explore and analyse their data using the business intelligence and data analytics tool known as Looker Studio. Users can develop and share interactive dashboards, reports, and visualizations that offer insights into their data using Looker Studio. Looker Studio features a drag-and-drop user interface, simple visualization tool, and is accessible even to non-technical users.

(We have a wide range of data sources that we can connect from Looker Studio.)

The flexibility of Looker Studio to connect to a range of data sources, such as databases, cloud services, and APIs, is one of its primary advantages. Users may now quickly access and analyze data from various sources on a single platform thanks to this. Additionally, Looker Studio provides strong data modelling and transformation capabilities, enabling users to convert unstructured data into actionable insights.

Organisations can use Looker Studio to make data-driven decisions based on current data insights. They are able to spot patterns, recognize anomalies, and make well-informed judgements that promote corporate expansion and success.

Let’s now discuss Big Query. Large datasets can be quickly and affordably analyzed using Google Cloud Platform’s Big Query, a cloud-based data warehousing and querying tool. Users of Big Query may store and analyze enormous amounts of data quickly and easily without the need for a sophisticated infrastructure or on-site hardware.

One of the key benefits of Big Query is its scalability. Big Query can handle petabytes of data and can be scaled up or down as needed, making it suitable for businesses of all sizes. Big Query also supports real-time data ingestion, allowing users to analyze data as it’s generated.

Big Query is designed to be easy to use and offers a powerful SQL-like query language that allows users to quickly analyze their data. It also offers a range of integration options, including with Looker Studio, making it easy for organizations to connect and analyse their data on a single platform.

(Inbuilt function of Big Query to explore results in Looker Studio)

Overall, Looker Studio and Big Query are both powerful tools for data analytics and can help organizations make data-driven decisions. By combining the two, organizations can access real-time data insights and unlock the full potential of their data.

Visualize Big Query data with looker studio using real-time connections:

There are a variety of ways to link Big Query data to Looker Studio; in this part, we’ll focus on the most effective ones.

Using the Looker Studio Connection “Custom Query Connector”:

This is the most effective technique to visualize the results of a big query. With the Custom Query Connector, you can extract and manipulate data in ways that are not possible with regular Looker connections, making it a powerful tool for working with Big Query data in Looker. However, since it necessitates a solid grasp of SQL and database connectivity, not all users may find it appropriate. But the Data Engineers and Data Analysts can easily feel the efficiency in this.

The Big Query project and dataset, as well as the SQL query that obtains the data, must be provided in order to set up the Custom Query Connector. Once the connection is made, you can use the data to build Looker models, views, and dashboards, and you can use mechanisms like caching and data refreshing to update them in real-time.

The scheduling feature of the custom query in looker studio, runs the query in Big Query and updates the dashboard at scheduled intervals.
When dealing with large amounts of data, it will be difficult to sort and filter when we establish this from the Looker studio end, but we can implement this from either end.

(The above picture shows the options to select when we do it from the looker studio end.)

Click on Big Query data source and then select custom query, as highlighted in the above picture.

Additionally, there is a second method for using the custom query connector on the BQ end; all we need to do is use the Big Query function “explore data using looker studio” after query is executed.

(The above picture shows the options to select when we do it from the Looker Studio end from the Big Query end. )

Click on “Explore Data” and then click on “Explore with Looker Studio” as highlighted in the above picture.

It is easy and straightforward to create BI dashboards using the Custom Query connector, and we can schedule it. Later, we can add more data sources using the same query or a different query, and we can merge the data using the “Edit Connection” tool in Looker Studio.

(The above picture shows the options to select to edit the connection in Looker Studio.)

Click on “Big Query Custom SQL” under Data Function and then click on “Edit Connection” as highlighted in the above picture.
When we choose “Big Query Custom SQL” under Data Sources and then click “edit”, a pop-up window al-lowing us to “edit the connection” to the data source appears.

There are a few other ways to connect Big Query with Looker Studio.

Using Google Cloud Pub/Sub to send data updates to Looker in real-time:

With Big Query as your data source, you can set up a Cloud Function that listens for data changes in Big Query and publishes those changes to the Pub/Subtopic. Once you’ve created a Pub/Sub topic and config-ured your data source to send data updates, you’ll need to set up a Looker connection that can receive those updates. This can be done by creating a new Looker connection and configuring it to use the Pub/Sub topic as its data source.

Using cache warming process, which preloads the Looker cache with the latest data at regular inter-vals:

Cache warming is the process of preloading Looker’s cache with the latest data at regular intervals. This improves the performance of Looker dashboards and visualisations by ensuring the most up-to-date data is readily available in the cache. The process involves scheduling the cache warming, running a script to populate the cache with the latest data, monitoring the process, and tuning it for efficiency.

Looker Studio with Big Query benefits:

Real-time data visualisation: Looker Studio provides real-time access to data stored in Big Query, ena-bling users to visualise and analyse data as it is updated in real-time.

Centralised data modelling: Looker Studio enables you to develop centralised data models that can be uti-lised by numerous teams and departments within your company, ensuring accuracy and consistency in your data analysis.

Customizable dashboards: Looker Studio enables you to create customised dashboards that can be tailored to the specific needs of different teams and departments, making it easier to share insights and drive data-driven decision-making.

Easy-to-use interface: The user-friendly interface on Looker Studio makes it simple for users to construct and edit dashboards and visualisations without the need for substantial technical expertise.

Scalability: Because Looker Studio is highly scalable, you can manage significant data volumes and meet rising user demand without sacrificing performance.

Integration with other tools: Data analysis may be easily included into your current processes thanks to Looker Studio’s seamless integration with a variety of other tools and technologies, including Google Cloud Platform and a wide range of third-party applications.

Overall, Looker Studio provides a powerful, flexible, and user-friendly platform for visualising and ana-lysing data stored in Big Query, enabling organisations to gain valuable insights and make data-driven de-cisions with greater speed and accuracy.

Looker Studio with the Big Query client use case saved a lot of time and effort.

Here is a challenge we learned about from one of our clients, the largest multiplex chain in India. When the team was considering a solution to identify pipeline failures for more than 50 production pipelines, I suggested using Looker Studio and suggested that we create a metadata table that collected data on the success and failure of runs and exceptions using pub/sub messages. From this metadata table, I built a complex query using windowing fun.

Conclusion:

Now that you are more familiar with the connections, you should be able to see how we can rapidly link our Big Query data to Looker Studio. Overall, Looker Studio with Big Query provides a scalable, flexible, and user-friendly platform for visualising and analysing data, making it the ideal choice for enterprises wishing to get insights and expedite decision-making with greater speed and accuracy.

In the next blogs, you will see other unique aspects of the integration of Big Query with Looker Studio.

Gratitude for reading. I hope this is helpful.

The post Looker Studio in Real-Time synchronization with Big Query 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.

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.

The post Power BI Meta Data extraction using Python 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

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Many businesses are finding their existing infrastructure that includes servers and racks to be limiting their ability to meet their need for increased storage space and compute power. In the traditional architecture, often businesses went for proprietary end-to-end solutions right from centralized data collection to storage, and analysis, to optimize resources and minimize costs but lost control of their own data.

As technology grows by leaps and bounds, businesses are also facing the problem of the tools being unable to up with the changing times and storage being insufficient for their growing needs. The cost of expanding infrastructure is also formidable, needing viable alternate solutions.

According to a McKinsey report, businesses are trying to simplify their current architectural approaches and accelerate deliveries across data activities such as acquisition, storage, processing, analysis, and exposure, to create a modern infrastructure that can support their future analytics and BI efforts. For this, six foundational shifts are being effected on data-architecture blueprints, leaving the core technology stack untouched. This can result in an increase in RoI due to lower IT costs, improved capabilities productivity, and lower regulatory and operational risk.

The Six Features of a Modern Data Ecosystem

As per the report, the six foundational shifts that facilitate the creation of a modern data ecosystem to support future analytics and BI efforts include:

1. Shifting to Cloud-based Data Platforms: Cloud-based solutions from providers such as Amazon, Azure Google, are disrupting the data architecture approach for sourcing, deployment, and running data infrastructure, platforms, and applications at scale. Two key components of this revolution are serverless data and containerized data solutions.

a. Serverless platforms such as Amazon S3 and Google BigQuery eliminate the need for installing and configuring solutions or managing workloads while enabling businesses to build and manage data-centric applications at scale at almost no operational overhead.

b. With containerized data solutions using Kubernetes, businesses can decouple compute power and data storage while automating the deployment of additional systems.

2. Real-time Data Processing: Real-time data streaming is a cost-effective solution that allows data consumers to receive a constant feed of the data they need by subscribing to relevant categories from a common data lake that is the source and retains all granular transactions. It can be of three types: messaging platforms such as Apache Kafka; streaming processing and analytics solutions such as Apache Spark Streaming, Apache Kafka Streaming, Apache Storm, and Apache Flume; alerting platforms such as Graphite or Splunk

3. Modular Platforms: A modular data architecture that leverages open source and best-of-breed components provides businesses with the flexibility to discard old technologies and embrace new ones with the least disruptions using data pipelines and API-based interfaces and analytics workbenches. They also facilitate the integration of disparate tools and platforms to connect with several underlying databases and services.

4. Decoupled Data Access: For effective reuse of data by different teams, businesses can provide limited and secure views and modify data access by exposing data through APIs. It also enables quick access to up-to-date and common data sets. As a result, analytics teams can collaborate seamlessly and accelerate the development of AI solutions. The two key components that facilitate this are an API management platform or API gateway and a data platform.

5. Domain-based Architecture: Instead of a central enterprise data lake, domain-driven data architecture designs help businesses customize and accelerate time-to-market new data products and services. Data sets can also be organized in a more easily consumable manner for domain users and downstream data consumers. Some of the enabling features of this architecture are its data-infrastructure-as-a-platform model integrated with data virtualization techniques, and data cataloging tools.

6. Flexible, Extensible Data Schemas: The traditional, predefined, and proprietary data models built into highly normalized schemas tend to be rigid, limiting the addition of new data elements or data sources due to the risk to data integrity. Therefore, with schema-light approaches with denormalized data models and fewer physical tables, data can be organized for optimizing performance, agility, and flexibility. This is facilitated by components and concepts such as Data-point modeling, graph databases, dynamic table structures, and JavaScript Object Notation (JSON).

This piece of content might be of your interest: Modern Data Analytics And Modern Data Architecture

Indium Approach

A McKinsey Digital report shows that only 14 percent of companies launching digital transformations are able to see sustained and substantial improvements in performance. This is because, though there are several digital engineering solutions and analytics service options available to draw insights through business intelligence and analytics, , organizations are unable to suit the right architecture for their business. What is popular or obvious may not be the right fit for them.

Indium Software is a data engineering specialist offering cross-domain and cross-functional expertise and experience to understand the unique needs of each business. We use commercial and open source tools based on the cost and business requirements to meet your unique data engineering and analytics needs.

To accelerate your data modernization journey to make your business future-ready, we help you with large-scale data transformation and Cloud Data & Analytics. To ensure the efficiency of your data modernization process, we help you with aligning your data management strategy with your business plan.

Our solution encompasses:

ETL Modernization: The three-step process of extract, transform, and load is designed to help you overcome your business challenges.

Data Governance: User- and role-based data access ensures data security, privacy, and compliance while enabling informed decision-making.

Data Visualization: Our experts use cutting-edge business intelligence solutions to enable data visualizations for actionable insights

Data Management: Data abnormalities are identified as they occur, reducing time and money in rectifying mistakes.

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