Change data capture is one of the fundamental underpinnings of modern data management. Without knowing when their enterprise data has changed or refreshed with new information, businesses wouldn’t be able to access up-to-the-minute insights that help them stay competitive in a constantly shifting landscape. In change data capture (CDC), users are promptly notified (either in real-time or near real-time) when changes have been made to a source table or source database.

In this guide, we'll discuss what a database schema is, six database schema designs, and how and why they are used. We know a lot of thought goes into database construction. Before creating any database, developers plan what it should include and how the different aspects work together. This planning ensures a database has the necessary design for its intended use. Coders then use the schema to implement the database’s design.

For organizations to succeed today, they have to make data-driven decisions and get the most value out of the information. “To monetize data, companies must first transform it so that it can be reused and recombined to enable new value creation.” Most organizations have a ton of information that is spread across multiple departments. Making sense of the information is no easy task. Inaccurate analytics or delayed insights can put the business in a vulnerable position.

Every computer application must have a method of storing, managing and using data. This requires an application and at least one database that can communicate with each other. Managing this connection can be difficult, especially with multiple databases. Fortunately, there are platforms that can manage databases and connections applications more efficiently. Heroku offers a Postgres management system for creating, managing, and using databases.

Organizations are losing out on data-driven decision-making opportunities when data stays in the data warehouse. While business intelligence solutions can surface insights from these data sets, it often reaches team members too late to be used for daily business operations. Reverse ETL empowers organizations to increase the value of their data warehouses through operationalization. Learn how this can transform the way companies use data and insights.

Have you been wanting to migrate your Heroku Postgres data to NetSuite but don’t know where to begin? We’ve put together a guide that will help make your migration seamless and included some tips to make the migration as smooth as possible.

Let's get started with this great debate. First, a step back; we’ve pointed out that Apache Spark and Hadoop MapReduce are two different Big Data beasts. The former is a high-performance in-memory data-processing framework, and the latter is a mature batch-processing platform for the petabyte scale. We also know that Apache Hive and HBase are two very different tools with similar functions. Hive is a SQL-like engine that runs MapReduce jobs, while HBase is a NoSQL key/value database on Hadoop.

The growth of corporate data and the need for more corporate applications and systems are not trends that will soon slow down. Data has become an essential component of commercial success and a measure of the value of a company. Investing in platforms, processes, and people that can effectively protect, transform, and leverage data is the hallmark of a modern data-driven enterprise.

Moving data from an organization’s systems into data warehouses and data lakes are essential to fuel business intelligence and analytics tools. These insights guide businesses into making decisions backed by data, allowing them to choose actions that have the best chance of positive growth. However, getting data from the source systems to these data stores can be a harrowing process.