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The DataOps Blog

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How to Make a Data Pipeline the Easy Way


With their ability to harness and make sense of all types of data from disparate sources, data pipelines are the foundation of modern analytics. A data pipeline refers to a series of steps (typically called jobs) that aggregate data from various sources and formats and validates this data in readiness for analytics and insights. Businesses can choose to build a…

Jesse Summan By July 19, 2022

AWS Reference Architecture Guide for StreamSets

Use Cases

Using StreamSets DataOps Platform To Integrate Data from PostgreSQL to AWS S3 and Redshift: A Reference Architecture This document describes the reference architecture for integrating data from a database to Amazon Web Services (AWS) data analytics stack utilizing the StreamSets DataOps Platform, including the StreamSets Data Collector and Transformer engines, as the data integration platform. It assumes a certain level…

Brenna Buuck By July 7, 2022

The 4 Best Data Integration Tools to Consider for 2022


We all know that data is the compass rose for business operations. From enabling marketing to fostering innovative design, data is the bedrock that allows companies to deliver innovative solutions to the marketplace. And much like every other aspect of business operations, using data to enhance business processes has become more sophisticated and complex because of ‌today’s technology-rich landscape. As…

By July 5, 2022

The Difference Between Data Governance and Data Management


Every day, business is becoming more dependent on data. From reports stating that the world’s data volume will grow by 40% per year to those that predict data analytics market growth of 13.54% CAGR, it's safe to say that data is and will continue to be central to business. And while data and business become ever more interdependent, it’s understandable…

By June 29, 2022

What You Need to Know About Kafka Stream Processing in Python


Instant notifications, product recommendations and updates, and fraud detection are practical use-cases of stream processing. With stream processing, data streaming and analytics occur in real-time, which helps drive fast decision-making. However, building an effective streaming architecture to handle data needs can be challenging because of the multiple data sources, destinations, and formats involved with event streaming. The Basics of Apache…

Brenna Buuck By June 23, 2022
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