Systems | Development | Analytics | API | Testing

2.5 quintillion bytes of data are produced every day, and those numbers are continually increasing. With such astronomical volumes of data, businesses have to understand and interpret data faster than ever before. However, data transfers must occur for businesses with millions of data entry points to properly store and interpret their data.

Consider for a moment, if you will, plastic patio furniture. Plastic Fantastic is a global manufacturer with several factories, warehouses, and plenty of stores. One can only imagine the sheer amount of data resulting from sales, production, suppliers, and finances. Everything that happens, from purchase and onward, to these chairs, tables, and cupboards in all corners of the world is measured.

Reverse ETL and customer data platforms (CDPs) are two big data trends that have been receiving a great deal of attention. While both CDPs and reverse ETL can help you make smarter data-driven decisions, there are also several crucial points of distinction. In this article, we’ll answer the question: what’s the difference between reverse ETL and a customer data platform?

Migrating analytics workloads to the public cloud has been one of the most significant big data trends in recent years—and it shows no sign of slowing down any time soon. According to a study by IT research company Forrester: Within three years, however, Forrester predicts that the fates will have reversed: Of course, before data can be processed in the public cloud, it has to get there in the first place via data migration.

On the surface, Talend seems like the ultimate data integration platform. It's open-source, maintains multi-cloud integration, supports data governance frameworks like GDPR and CCPA, and handles both ETL and ELT, providing you with more flexibility for data management. Dig a little deeper, though, and you'll notice this platform has an outdated user interface and limited capabilities, and you'll probably need to upgrade to its enterprise version to execute data integration.

Apache Hive and Apache HBase are incredible tools for Big Data. While there is some overlap in their functions, both Apache Hive and Apache HBase have unique qualities that make them better suited to specific tasks. Some key differences include: Ultimately, comparing Apache Hive to Apache HBase is like comparing apples to oranges or Google to Facebook. While the two entities are similar, they don't provide users with the same functionality.

APIs are powerful for ETL (extract, transform, load) and data integration workflows. API integrations make it possible for the seamless exchange of information between websites, databases, and applications. The Xplenty API allows you and your enterprise to monitor Xplenty clusters and jobs. Through the Xplenty data processing package and Xplenty web application, you can call the Xplenty API to.

Data warehouses fixed one aspect of the data silo problem but introduced another. They function as a large, single source of truth for your organization, but getting insights from this data in a typical Extract, Transform, Load (ETL) data pipeline requires the use of Business Intelligence (BI) and analytics platforms. By the time your data team creates these reports and sends them to other business units, it’s too late for daily decision-making.

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” That’s according to the author and consultant Geoffrey Moore. It’s an unsettling thought given that data and analytics are shifting from a secondary activity to a core business function. So what can companies do to gather and harness all of this information? The below guide discusses data sources and their role in informing decision-making.

With over 100 out-of-the-box connectors, Fivetran is the no-code/low-code solution for many CTOs, software developers, and data analysts who want to extract, load, and transform data from disparate sources, load that data into a warehouse for analytics, and then convert it into a usable format.