Systems | Development | Analytics | API | Testing

April 2022

Automate your Reports on Google Sheets with Hevo Activate

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.

Building and Managing the Modern Datastore: The Data Lakehouse

The 'data lakehouse' is quickly becoming popular in the data analytics community. Data lakehouse architecture combines the benefits of a data warehouse and a data lake. It aims to merge the data warehouse’s data structure and management features along with the flexibility and relatively low cost of the data lake. Watch this panel discussion to learn how the data lakehouse can address the limitations of the data lake and data warehouse architecture to deliver significant value for organizations. Explore why the data lakehouse is an ideal option for enterprise data storage initiatives.

Build Robust and Efficient Analytics Engine with Hevo's Data Transformation

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.

Data Warehouse Automation: What, Why, and How?

Building a data warehouse is an expensive affair and it often takes months to build one from scratch. There is also a constant struggle to keep up with the large volumes of data that is constantly generated. On top of that, setting up a strong architectural foundation, working on repetitive and mundane data validation tasks and ensuring data accuracy is another challenge. This puts tremendous stress on data teams and data warehouses. Data warehouse automation is intended to handle this growing complexity.

Building Product Analytics At Petabyte Scale

Product analytics is the most critical and complex task for any product team. There are thousands of data points that have to be analyzed carefully while setting up the product analytics foundation and it enables product teams to use data to track, visualize, and analyze user engagement and behavior that can be used to improve and optimize a product experience. However, managing large data workloads can be very challenging as not all data that is collected can be directly used for analytics.

Data Warehouse Automation: What, Why, and How?

Data Warehouse Automation helps IT teams deliver better and faster results by getting rid of repetitive design, development, deployment and operational tasks within the data warehouse lifecycle. With automation, organizations can accelerate the data to the analytics journey, work more effectively with large amounts of data and save cost. Join this session with Darshan Wakchaure, Global Data & Analytics Competency Head, Tech Mahindra as he shares his insights on the key benefits of Data Warehouse Optimization and how to achieve Data Warehouse Automation at scale.

Best 15 ETL Tools in 2023

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.

Apache Kafka to BigQuery: 2 Easy Methods

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.