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.
Updated: June 2022
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.
Since 2020 organizers have been opting for online virtual conferences instead of in-person conferences. In 2021 and 2022 the same trend continues although some conferences are also being scheduled to be attended in person since the second half of the year 2021.
Data science conferences are one of the best ways to learn, develop new skills, meet and discuss ideas and discover how others are applying AI, analytics and machine learning in their work.
Here are several conferences for data scientists you should consider attending.
Updated: May 2023
When trying to assess how knowledgeable a developer is in general and in JDBC in particular, here’s a question I like to ask: how would you speed up inserts when using JDBC?
Here are some options to consider if you ever need to insert data quickly into an SQL database.
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.
Continue reading
One feature of Data Pipeline is its ability to aggregate data without a database. This feature allows you to apply SQL “group by” operations to JSON, CSV, XML, Java beans, and other formats on-the-fly — in real-time. This quick tutorial will show you how to use the GroupByReader class to aggregate Twitter search results.
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 now includes a new AsyncMultiReader endpoint that lets you read from multiple DataReaders in parallel. Here’s how it works.
Data Pipeline makes it easy to read, transform, and write XML and Excel files. This post shows you how you too can load data from an on-disk XML file, apply transformations on the fly, and save the result to an Excel file.
Data Pipeline lets you read, write, and convert Excel files using a very simple API. This post will show you how to create Excel files containing more than one work sheet or tab.
