Building a Cloud ETL Pipeline on Confluent Cloud
As enterprises move more and more of their applications to the cloud, they are also moving their on-prem ETL pipelines to the cloud, as well as building new ones. There
As enterprises move more and more of their applications to the cloud, they are also moving their on-prem ETL pipelines to the cloud, as well as building new ones. There
This article shows how you can offload data from on-premises transactional (OLTP) databases to cloud-based datastores, including Snowflake and Amazon S3 with Athena. I’m also going to take the opportunity
Kafka Connect is the part of Apache Kafka® that provides reliable, scalable, distributed streaming integration between Apache Kafka and other systems. Kafka Connect has connectors for many, many systems, and
Trains are an excellent source of streaming data—their movements around the network are an unbounded series of events. Using this data, Apache Kafka® and Confluent Platform can provide the foundations
Red Pill Analytics was recently engaged by a Fortune 500 e-commerce and wholesale company that is transforming the way they manage inventory. Traditionally, this company has used only a few
The way in which we handle data and build applications is changing. Technology and development practices have evolved to a point where building systems in isolated silos is somewhat impractical,
KSQL, the SQL streaming engine for Apache Kafka®, puts the power of stream processing into the hands of anyone who knows SQL. It’s fun to use for exploring data in
Typically, an enterprise service bus (ESB) or other integration solutions like extract-transform-load (ETL) tools have been used to try to decouple systems. However, the sheer number of connectors, as well
In this post I’m going to show what streaming ETL looks like in practice. We’re replacing batch extracts with event streams, and batch transformation with in-flight transformation. But first, a
There has been a lot of talk recently that traditional ETL is dead. In the traditional ETL paradigm, data warehouses were king, ETL jobs were batch-driven, everything talked to everything
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