Sometime in 2019, Netflix cracked a conundrum that stumped them for years. The company had so much data about its content and subscribers, it had to sync multiple heterogeneous data stores like MySQL and Elasticsearch continuously, which brought seriously stressful challenges like dual writes and distributed transactions. So Netflix created its own CDC tool that processes captured log events in sequence and takes dumps for specific tables and primary keys of tables. Problem sorted. Case closed.

Reverse ETL is a data integration technology that offers a wonderful way to enable solutions for making various stored data more actionable and usable. This process is especially helpful for enterprise business operation tools that help teams execute processes and meet goals more effectively. The idea is to use clean and accurate data to enhance various SaaS platforms and business management tools to enhance processes.

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.

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.

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.

CRM (customer relationship management) software is the lifeblood of any modern B2C company. By monitoring and storing all of your interactions with prospects and customers—from their first visit to your website to their most recent purchase—CRM software makes it dramatically easier to segment your customer base, identify hidden trends in the data, make smarter predictions, and forecasts, and much more.

Thanks to its user-friendliness and popularity in the field of data science, Python is one of the best programming languages for ETL. Still, coding an ETL pipeline from scratch isn’t for the faint of heart — you’ll need to handle concerns such as database connections, parallelism, job scheduling, and logging yourself. The good news is that Python makes it easier to deal with these issues by offering dozens of ETL tools and packages.

Every organization wants to stay on the cutting edge of technology, making smart and data-driven decisions. However, ensuring that company information and data integration remains up to date can be a very time-consuming process. That is where CDC can make all the difference. Change data capture or CDC allows for real-time data set changes, ensuring that company data is always up to date. Change data capture can transform the way companies make data-driven decisions.

For more than three decades, Microsoft SQL Server has helped countless organizations store and manage their enterprise data, and it’s still one of the most widely used software applications on the planet. According to the DB-Engines database ranking, SQL Server remains the third most popular database management system, just behind Oracle and MySQL. Change data capture (CDC) is essential functionality for many businesses, especially those with real-time ETL use cases.

Working with raw or unprocessed data often leads to poor decision-making. This explains why data scientists, engineers, and other analytic professionals spend over 80% of their time finding, cleaning, and organizing data. Accordingly, the ETL process - the foundation of all data pipelines - devotes an entire section to T, transformations: the act of cleaning, molding, and reshaping data into a valuable format.