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.
With data being produced from many sources in a variety of formats it’s imperative for businesses to have a sane way to gain useful insight. Data integration is the process of transforming data from one or more sources into a form that can be loaded into a target system or used for analysis and business intelligence.
Machine learning and artificial intelligence in general are two of today’s hottest skills. AI and ML conferences provide a place for you to improve your skills, discuss trends, and exchange ideas with other data scientists, developers, and entrepreneurs. Whether you’re new to the world of machine learning, trying to stay up-to-date, or just looking to network, there’s a conference happening for you. This article lists over 50 conferences taking place around the world for you to consider attending.
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.
Being a data scientist means dedication to continuous learning. One great way to keep learning, improve your network, and get exposed to different views is to attend conferences. Here are several conferences for data scientist you should consider attending.
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
Data Pipeline v3.1.4 is now available for download. This release includes support for MySQL upserts, lower JSON and XML memory usage, bug fixes, and more.
Data Pipeline 3.1 is now available for download. This is a milestone release that adds native support for hierarchical data (nested records and multidimensional arrays).
Data Pipeline makes it easy to read, transform, and write XML and Excel files. This post demonstrates how to load data from an on-disk XML file, apply transformations on-the-fly, and save the result to an Excel file.
We’re pleased to announce the release of version 3.0 of our Data Pipeline engine.
This release includes the new Sliding Window Aggregations feature to perform continuous SQL group-by operations on streaming data.
We’ve improved the performance of the XPath based readers (JsonReader, XmlReader, and JavaBeanReader), included new conveniences to reduce your code size, and added several new transformers and filters.
We’re also now offering a free 30-day trial for you to take the premium and enterprise features out for a test drive.