Setting up a Data Analytics Platform on Cloud

Creating a cloud-based Data Analytics Platform can remove the barriers, not only streamlining your data operations but also empowering your company to manage its entire end-to-end data analytics journey. The cloud data warehouse will most likely be your company’s biggest expenditure in terms of technology to help you on this path. It’s critical to concentrate on extracting the maximum value from your cloud data warehouse. Furthermore, many of the industry’s top cloud data warehouses, such as Amazon Redshift, Google BigQuery, and Snowflake, have robust partner ecosystems that offer appropriate third-party solutions that integrate with your existing cloud infrastructure.  

Starting with numerous Data Sources, an Integration and Transformation Layer, a Cloud Data Warehouse, and the Visualization and Analysis Layer, the section below talks more of these Data Analytics Platform components one by one: 

Architecture Diagram – Data Analytics Platform 

Data Sources: 

There are few commonly use data sources in nowadays enterprise environment like Data Lake, API’s, Semi-Structured data. A data lake is a storage area that holds a vast volume of unprocessed data until it is needed. A data lake uses a flat architecture to store data, whereas a hierarchical data warehouse stores data in files or folders. Modern data analytics platforms frequently require the capacity to load XML, JSON, or even spreadsheet files, which is like data lakes, which are generally made up of semi-structured files. Select tool sets capable of loading Cloud Data Warehouse’s from these files. Integrations between your firm and significant third-party resources like Salesforce or Google Analytics are conceptually like Application Programming Interfaces. 

Integration and Transformation Layer: 

Data Analytics Platform’s depend significantly on solutions that can combine the data sources indicated above, among others, and ETL packages for the Extract, Transformation, and Load activities such software enables. Not only does this necessitate the capacity to extract and load data from several platforms and get it into the Cloud Data Warehouse, but it also necessitates the ability to alter the resulting data into structures that are best suited for extrapolating insights.

Visualization and Analysis: 

The visualization and analytics layer are the Data Analytics Platform’s final component. Based on the final, modified data we’ve pulled from our source systems, and augmented with other data elements to provide meaningful business intelligence, this layer provides both formal and ad-hoc reporting options. The visualizations and analyses that arise enrich the data-driven decision-making process, allowing for smarter judgments and, eventually, competitive advantages. Tableau, Looker, AWS Quicksight, and Google Data Studio are some of the better visualization and reporting tools. 

To maximize time to value, use cloud flexibility, take advantage of computation and storage resources, and minimize costs, we can use the following steps for Data Analytics Platform: 

Steps for getting started with Data Analytics Platform: 

1. Choose a cloud-based data warehouse that suits your present and future data requirements. Amazon Redshift, Google BigQuery, and Snowflake were the current market leaders at the time of drafting this whitepaper. 

2. Determine the data sources and applications your company will employ to populate your cloud data warehouse. If the alternatives on your short list charge for licensing or connectivity, make sure to factor in present and future fees in your estimated total cost of ownership. 

3. Choose a data transformation method. A data transformation solution that also manages data loading is something we advocate. This will help you simplify your DAP and streamline your data travel. To enhance resiliency, look for solutions that lessen your need to hand code. 

4. If you haven’t already done so, choose a BI and analytics tool. Looker, Tableau, AWS Quicksight, and Data Studio are some of the market’s current leaders. 

5. Create a proof of concept (PoC) engagement to evaluate your data migration and transformation approach, as well as the quality of your data when it has been completed. 

6. Follow through on your data migration and transformation plan. 

7. Connect your business intelligence tool to your data warehouse, and so forth.