Where change is welcome.
Following on from last week's guest post from MapR's Ian Downard on integrating StreamSets Data Collector with MapR Persistent Application Client Container (PACC), MapR Distinguished Technologist John Omernik offers a cautionary tale on examining your assumptions before jumping into the world of…
Today's guest blogger is Ian Downard, a Senior Developer Evangelist at MapR Technologies. Ian focuses on machine learning and data engineering, and recently documented how he brought together the MapR Persistent Application Client Container (PACC) with StreamSets Data Collector and Docker to build pipelines…
Kinetica, just one of dozens of origins and destinations supported by StreamSets Data Collector, is a distributed, in-memory, GPU database designed for geospatial analysis, machine learning, predictive analytics, and other workloads requiring high performance parallel processing. Mathew Hawkins, a Principal Solutions Architect at Kinetica, recently…
In this guest blog, Predera‘s Kiran Krishna Innamuri (Data Engineer), and Nazeer Hussain (Head of Platform Engineering and Services) focus on building a data pipeline to perform lookups or run queries on Hive tables with the Spark execution engine using StreamSets Data Collector and Predera’s custom Hive-JDBC lookup processor.
This post was originally published on the Cloudera VISION blog by Sam Heywood. StreamSets configurations and images of Apache Spot Open Data Model ingest pipelines can be found here on Github.
A quick conversation with most Chief Information Security Officers (CISOs) reveals they understand they need to modernize their security architecture and the correct answer is to adopt a machine learning and analytics platform as a fundamental and durable part of their data strategy. However, many CISOs fear deployment of an initial use case will be somewhat daunting. Cloudera has partnered along with Arcadia Data and StreamSets to make it easier than ever for CISOs to take the first step and deploy basic use cases leveraging data sources common to many environments.
Mike Fuller, a consultant at Red Pill Analytics, recently wrote Stream Me Up (to the Cloud), Scotty, a tutorial on installing StreamSets Data Collector (SDC) on Amazon Web Services EC2. Mike’s article takes you all the way from logging in to a fresh EC2 instance to seeing your first pipeline in action. We’re reposting it here courtesy of Mike and Red Pill.
Sandish Kumar, a Solutions Engineer at phData, builds and manages solutions for phData customers. In this article, reposted from the phData blog, he explains how to generate simulated NetFlow data, read it into StreamSets Data Collector via the UDP origin, then buffer it in Apache Kafka before sending it to Apache Kudu. A true big data enthusiast, Sandish spends his spare time working to understand Kudu internals.
Apache Kudu and Open Source StreamSets Data Collector Simplify Batch and Real-Time Processing
As originally posted on the Cloudera VISION Blog.
At StreamSets, we come across dataflow challenges for a variety of applications. Our product, StreamSets Data Collector is an open-source any-to-any dataflow system that ensures that all your data is safely delivered in the various systems of your choice. At its core is the ability to handle data drift that allows these dataflow pipelines to evolve with your changing data landscape without incurring redesign costs.
This position at the front of the data pipeline has given us visibility into various use cases, and we have found that many applications rely on patched-together architectures to achieve their objective.
Big data has come a long way, with adoption accelerating as CIOs recognize the business value of extracting insights from the troves of data collected by their companies and business partners. But, as is often the case with innovations, mainstream adoption of big data has exposed a new challenge: how to ingest data continuously from any source and with high quality. Indeed, we have found that there are environmental causes that make it next to impossible to scale ingestion using current approaches, and this has serious implications for scaling big data projects.
Now we’ll start publishing messages to a JMS queue. They are simple text messages with random words. Periodically the program outputs two types of bad records. Records without an id message header and records with empty content. We will use…