Usually, your business users request you share the business reports in Spreadsheets. They are highly familiar with Sheets and prefer their reports on Sheets only. They assume delivering reports in XLS format is easy and quick. But, we understand the efforts and time required to export reports to Spreadsheets. Every time, you will have to run queries on your centralized data at the warehouse and then export results in XLS format. You may need to edit and update the Sheet regularly.
In today’s digital age, robust and faster data analytics is essential for your organization’s growth and success. The faster you deliver analytics-ready data to your analyst, the faster they can analyze and derive insights. Though you would have adopted the ELT process with EL data pipelines to load data quickly to the warehouse, your team would still face inefficient and delayed analysis.
ETL stands for Extract, Transform, and Load. It is defined as a Data Integration service and allows companies to combine data from various sources into a single, consistent data store that is loaded into a Data Warehouse or any other target system. ETL serves as the foundation for Machine Learning and Data Analytics workstreams. Through multiple business rules, ETL organizes and cleanses data in a way that caters to the Business Intelligence needs, like monthly reporting.
Organizations today have access to a wide stream of data. Data is generated from recommendation engines, page clicks, internet searches, product orders, and more. It is necessary to have an infrastructure that would enable you to stream your data as it gets generated and carry out analytics on the go. To aid this objective, incorporating a data pipeline for moving data from Apache Kafka to BigQuery is a step in the right direction.