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Data is the fuel for applications, services and decisions across an organization, but far too often that fuel is stuck in a silo or takes too long to reach where it’s needed in order to be most effective. Traditional batch data ingestion is past its sell-by date in the age of always-on customer experiences, where the expectation is for real-time, up-to-the-minute updates based on the freshest data. In this demo-heavy webinar you'll learn why streaming ETL is essential for modern businesses, why current batch architectures with monolithic data warehouses are out of date and how Decodable is approaching the challenges traditionally associated with Streaming ETL. You'll also see how to build a streaming pipeline in minutes and then replicate that experience for yourself with a free account at http://decodable.co - Start Free!
You can get started with Decodable for free - our developer account includes enough for you to build a useful pipeline in minutes, not months.
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We’re Abusing The Data Warehouse; RETL, ELT, And Other Weird Stuff.
By now, everyone has seen the rETL (Reverse ETL) trend: you want to use data from app #1 to enrich data in app #2. In this blog, Decodable's founder discusses the (fatal) shortcomings of this approach and how to get the job done.
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A Practical Introduction to the Data Mesh
There’s been quite a bit of talk about data meshes recently, both in terms of philosophy and technology. Unfortunately, most of the writing on the subject is thick with buzzwords, targeted toward VP and C-level executives, unparsable to engineers. The motivation behind the data mesh, however, is not only sound but practical and intuitive.
Decodable Platform Overview
I joined Decodable about a year ago as one of the founding engineers. One year on, we’ve built a self-service real-time data platform supporting hundreds of accounts in multiple regions. In this blog post, I’ll share why we built Decodable, the design decisions we made, and the platform architecture.
Top 6 Patterns for Ingesting Data Into Druid
Most people fold their laundry before putting it into the drawers, for the simple reason that once the object is in the container, it is then constrained by the limits of the container. Similarly, users working to ingest data into Druid will find that it’s much preferable to pre-process the data. Here are the top 6 reasons to pre-process your streams.
The Top 5 Streaming ETL Patterns
ETL and ELT are traditionally scheduled batch operations, but as the need for always-on, always-current data services becomes the norm, realtime ELT operating on streams of data is the goal of many organizations - if not the reality, yet.In real world usage, the ‘T’ in ETL represents a wide range of patterns assembled from primitive operations. In this blog we’ll explore these operations and see examples of how they’re implemented as SQL statements.
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