Securely and reliably build custom connectors for any data source to ensure no data source is left siloed or underutilized.

Many enterprises face significant challenges when it comes to building data pipelines in AWS, particularly around data ingestion. As data from diverse sources continues to grow exponentially, managing and processing it efficiently in AWS is critical. Without these capabilities, it’s harder to analyze and get any meaning from your data.

Data pipeline automation plays a central role in integrating and delivering data across systems. The architecture is excellent at handling repetitive, structured tasks, such as extracting, transforming, and loading data in a steady, predictable environment, because the pipelines are built around fixed rules and predefined processes. So, they will continue to work if you maintain the status quo, i.e., as long as your data follows a consistent structure.

How to leverage Fivetran and Starburst to supercharge your data lakehouse with end-to-end data pipelines.

In today’s world, innovation doesn’t happen in a vacuum; collaboration can help technological breakthroughs happen faster. The rise of AI, for example, will depend on the collaboration between data and development. We’re increasingly seeing software engineering workloads that are deeply intertwined with a strong data foundation.

In large organizations, Confluent Cloud is often simultaneously accessed by many different users along with business-critical applications, potentially across different lines of business. With so many individual pieces working together, the risk of an individual outage, error, or incident affecting other services increases. An incident could be constituted by a user clicking a wrong button, an application’s misconfiguration, or just a bug—you name it.

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems.

IT security professionals need to recognize the critical importance of securing data pipelines in a data-driven corporate environment.