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data integration and change data capture
This article was originally posted on Towards Data Science here. Data integrations have been around for decades, but there has been a recent explosion of new, compelling data integration companies offering cloud-native, easy-to-configure connectors and quick access to high-value analytics. These offerings come from companies like Fivetran, Stitch Data, Matillion, and Airbyte. It can be difficult for any data team to know whether to stick to their existing data stack or to adopt these new tools. Though it’s great to see data integrations becoming more mainstream and new approaches emerging, we think there’s a growing gap being created by new
data lakehouses
Data warehouses, data lakes — and now data lakehouses. What’s next? The technology to manage your company’s data is changing so rapidly, it can be difficult to know what to do about new vendors bringing innovative data services to the market. Can they plug into your data stack? Will they compromise your data security strategy? If you have a data lake and a data warehouse, should you consolidate into a lakehouse? Is there something even newer on the market you’re missing? That’s a lot to think about. We’re here to help. We asked Datacoral’s CEO Raghu Murthy some key
change data capture
Highlights from the “Real World Change Data Capture” Podcast (Part 2)  In our previous article, What Is Change Data Capture (CDC) Part 1, we highlighted several Change Data Capture-related questions and answers from episode 177 of the Data Engineering Podcast. Tobias Macey hosted Datacoral’s founder and CEO, Raghu Murthy, and spent 50 minutes discussing Datacoral’s real-world usage of Change Data Capture data pipelines. In this article, we go a little deeper with CDC by covering: Alternatives to CDC and use cases for more batch-oriented approaches What to consider before you implement CDC and the barriers to entry Shortcomings and

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