StreamSets Data Integration Blog
Where change is welcome.
Why Financial Services Needs ESG Data Integration — and How to Get Agile
In the not-so-distant past, profits and shareholder value trumped all other concerns for corporations. But a new generation of buyers has put increasing pressure on companies to do more and want to invest in and do business with corporations making a positive impact on the world. Today, businesses demonstrate this impact through ESG reporting on environmental, social, and governance criteria.…
Azure Synapse vs. Databricks vs. Azure Databricks
Data lakehouses are robust data platforms that unify the best features of data warehouses and lakes to produce a rich data solution. One such platform is Databricks. Built using open standards, it helps unify your storage, analytics, and Artificial Intelligence (AI) workloads. Databricks integrates with cloud platforms; for example, Azure Databricks is an integration of the Databricks platform and Azure,…
How To Automate Data Integration and Prevent Engineering Burnout
An overwhelming 97% of data engineers report burnout in their day-to-day jobs, citing repetitive tasks and fixing errors in the data lifecycle as the most common reason. For most organizations today, data arrives in droves from multiple locations. Manually loading these data for integrating these data into one central location for decision-making is far too time-consuming. Applying automation improves the…
A Ticking Time Bomb: Why Organizations Need To Regain Control of Their Data Pipelines
Modern enterprises are operating in the “wild west” when it comes to data. Applying controls consistently across today’s distributed data architectures is critical to successful digital transformation. The complexity of hybrid and multi-cloud environments, fragmentation of data supply chains, and confusion over who should be responsible for managing data all add up to one thing: chaos. This chaos prevents organizations…
Mastering Data Lake Ingestion: Methods and Best Practices To Know
Data is a valuable business asset, lying at the center of decision-making, customer satisfaction, and product development. However, data remains useless before it’s processed. The first step in processing involves extracting and transferring data from where they’re generated to storage locations/staging areas like data lakes or warehouses for use through data ingestion. Data ingestion is similar to an ETL or…