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A Fun Example of Streaming Data into Minecraft

By March 27, 2018

Angel AlvaradoAngel Alvarado is a senior software engineer at One Degree, a San Francisco-based non-profit, and also helps run the Molanco data engineering community. In his spare time, Angel enjoys playing Minecraft with his 11 year-old-cousin. Recently, Angel, found a fun way to combine his gaming with data engineering. This blog entry, reposted from the original with Angel’s kind permission, picks up the story…

Data Engineering can get really complex really quick and being aware of the hundreds of tools and data platforms in the industry can get very overwhelming. The following project is about how to use three data engineering tools to visualize data in a video game, it aims to solve a common data engineering problem with a twist to make it fun and entertaining.

Managing Data Operations on the Edge

By February 20, 2018

lightweight agentTogether, StreamSets Control Hub (SCH) and StreamSets Data Collector Edge (SDC Edge) allow you to create, deploy and run dataflow pipelines in an unprecedented variety of environments. In this short series of videos, I’ll show you how to install SDC Edge on a Raspberry Pi, how to get started building edge pipelines with SCH’s Pipeline Designer, and how SDC Edge and its big brother, StreamSets Data Collector (SDC) work together to move data all the way from IoT sensors to the heart of your data infrastructure.

Streaming Data from Twitter for Analysis in Spark

By January 10, 2018

FootballHappy New Year! Our first blog entry of 2018 is a guest post from Josh Janzen, a data scientist based in Minnesota. Josh wanted to ingest tweets referencing NFL games into Spark, then run some analysis to look for a correlation between Twitter activity and game winners. Josh originally posted this entry on his personal blog, and kindly allowed us to repost it here. Over to you, Josh:

Tis the season of NFL football, and one way to capture excitement is Twitter data. I’ve tickered around with Twitter’s Developer API before, but this time I wanted to use a streaming product I’ve heard good things about: StreamSets Data Collector.

Fun with FileRefs – Manipulating Whole File Data

By November 2, 2017

rot13 processorAs well as parsing incoming data into records, many StreamSets Data Collector (SDC) origins can be configured to ingest Whole Files. The blog entry Whole File Transfer with StreamSets Data Collector provides a basic introduction to the concept.

Although the initial release of the Whole File feature did not allow file content to be accessed in the pipeline, we soon added the ability for Script Evaluator processors to read the file, a feature exploited in the custom processor tutorial to read metadata from incoming image files. In this blog post, I’ll show you how a custom processor can both create new records with Whole File content, and replace the content in existing records.

Evolving Avro Schemas with Apache Kafka and StreamSets Data Collector

By October 25, 2017

Avro LogoApache Avro is widely used in the Hadoop ecosystem for efficiently serializing data so that it may be exchanged between applications written in a variety of programming languages. Avro allows data to be self-describing; when data is serialized via Avro, its schema is stored with it. Applications reading Avro-serialized data at a later time read the schema and use it when deserializing the data.

While StreamSets Data Collector (SDC) frees you from the tyranny of schema, it can also work with tools that take a more rigid approach. In this blog, I’ll explain a little of how Avro works and how SDC can integrate with Confluent Schema Registry’s distributed Avro schema storage layer when reading and writing data to Apache Kafka topics and other destinations.

Cache Salesforce Data in Redis with StreamSets Data Collector

By July 6, 2017

Redis LogoRedis is an open-source, in-memory, NoSQL database implementing a networked key-value store with optional persistence to disk. Perhaps the most popular key-value database, Redis is widely used for caching web pages, sessions and other objects that require blazingly fast access – lookups are typically in the millisecond range.

At RedisConf 2017 I presented a session, Cache All The Things! Data Integration via Jedis (slides), looking at how the open source Jedis library provides a small, sane, easy to use Java interface to Redis, and how a StreamSets Data Collector (SDC) pipeline can read data from a platform such as Salesforce, write it to Redis via Jedis, and keep Redis up-to-date by subscribing for notifications of changes in Salesforce, writing new and updated data to Redis. In this blog entry, I’ll describe how I built the SDC pipeline I showed during my session.

Triggering Databricks Notebook Jobs from StreamSets Data Collector

By June 21, 2017

S3 and DatabricksLast December, I covered Continuous Data Integration with StreamSets Data Collector and Spark Streaming on Databricks. In StreamSets Data Collector (SDC) version 2.5.0.0 we added the Spark Executor, allowing your pipelines to trigger a Spark application, running on Apache YARN or Databricks. I’m going to cover the latter in this blog post, showing you how to trigger a notebook job on Databricks from events in a pipeline, generating analyses and visualizations on demand.

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