Welcome to the DataPipeline 7.0 release. Since our last update, the DataPipeline team has been hard at work adding more declarative components, new integrations, new transformations, and generally making the framework easier to use. Our goal is to make simple use-cases easy and complex ones less difficult to implement.
We’re pleased to announce the release of DataPipeline version 6.0. This release includes our new DataPipeline Foundations addon that brings decisioning, source-target data mapping, and other cool features to your software.
Today we’re pleased announce the release of Data Pipeline version 4.4. This update includes integration with Amazon S3, new features to better handle real-time data and aggregation, and new XML and JSON readers to speed up your development.
We’re building on a new tool to help you work faster with Data Pipeline.
This new tool is a web app that lets you interactively transform, filter, and prepare data on-the-fly. It also lets you generate Data Pipeline code based on the actions you perform.
We recently received an email from a Java developer asking how to convert records in a table (like you get in a relational database, CSV, or Excel file) to a composite tree structure. Normally, we’d point to one of Data Pipeline’s XML or JSON data writers, but for good reasons those options didn’t apply here. The developer emailing us needed the hierarchical structures in object form for use in his API calls.
Since we didn’t have a general purpose, table-tree mapper, we built one. We looked at several options, but ultimately decided to add a new operator to the GroupByReader. This not only answered the immediate mapping question, but also allowed him to use the new operator with sliding window aggregation if the need ever arose.
The rest of this blog will walk you through the implementation in case you ever need to add your own custom aggregate operator to Data Pipeline.
Updated: July 2021
ETL is a process for performing data extraction, transformation and loading. The process extracts data from a variety of sources and formats, transforms it into a standard structure, and loads it into a database, file, web service, or other system for analysis, visualization, machine learning, etc.
ETL tools come in a wide variety of shapes. Some run on your desktop or on-premise servers, while others run as SaaS in the cloud. Some are code-based, built on standard programming languages that many developers already know. Others are built on a custom DSL (domain specific language) in an attempt to be more intentional and require less code. Others still are completely graphical, only offering programming interfaces for complex transformations.
What follows is a list of ETL tools for developers already familiar with Java and the JVM (Java Virtual Machine) to clean, validate, filter, and prepare your data for use.
Earlier this year a friend sent me a video showing how he implemented a phone bill calculation challenge using Scala. I took a stab at it using Java + Data Pipeline and below is what I came up with.
How about you? How would you code this using your favourite language or framework?
Updated: July 2021
If you have ever tried to export emails to Excel for analysis, you know it is not exactly straightforward. Maybe you need to find the top companies contacting you and your sales team. Maybe you need to perform text or sentiment analysis on the contents of your messages. Or maybe you’re creating visualizations to better understand who’s emailing you. This east guide will show you how you can use Data Pipeline to search and read emails from Gmail or G Suite, process them any way you like, and store them in Excel.
Updated: July 2021
Most examples of creating a Spring Batch ETL Job require an enormous amount of code for such a routine task. In this blog, I will show you how to accomplish the same task of summarizing a million stock trades to find the open, close, high, and low prices for each symbol using our Data Pipeline framework.
We’re excited to introduce Data Pipeline version 4.1, the second update on our 2016 roadmap.
This release features MongoDB integration, expression language additions, and improved transformations and joins. We’ve also thrown in a ton of examples for all the new 4.1 and 4.0 features. Enjoy. Continue reading