In today’s microservice revolution, where software applications are designed as independent services that work together, two technologies stand out. Docker, the defacto standard for containerization, and Kubernetes, a container orchestration and management tool. In this blog I will explain how to run StreamSets Data Collector (SDC) Docker containers on Kubernetes.
MapR-DB is an enterprise-grade, high performance, NoSQL database management system. As a multi-model NoSQL database, it supports both JSON document models and wide column data models. MapR-DB stores JSON documents in tables; documents within a table in MapR-DB can have different structures. StreamSets Data Collector enables working with MapR-DB documents with its powerful schema-on-read and ingestion capability.
With StreamSets Data Collector, I’ll show you how easy it is to stream data from MongoDB into a MapR-DB table as well as stream data out of the MapR-DB table into MapR Streams.
Reposted from the Cloudera Vision blog.
What do Sony, Target and the Democratic Party have in common?
Besides being well-respected brands, they’ve all been subject to some very public and embarrassing hacks over the past 24 months. Because cybercrime is no longer driven by angst-ridden teenagers but rather professional criminal organizations and state-sponsored hacker groups, the halcyon days of looking for a threat signatures are well behind us.
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.
I am always eager to learn about new architectures and best big data practices. Recently I came across a paper from Trifacta discussing the role of data preparation and it got me thinking about the complementary nature of data ingestion and data preparation.
Data preparation, more colorfully known as data wrangling, is the activity performed by data-driven professionals, such as data or business analysts, to explore, clean, transform and blend data of all varieties to make it trustworthy for analysis or predictive modeling. A form of data manipulation that has traditionally been achieved using Excel or, for more technically-advanced end users, languages such as R, SAS or Python. But with the rise of enormous and dynamic data sets in Hadoop, these approaches are no longer feasible. Trifacta took the lead in creating a self-service web-based solution that enables business users to access and manipulate data stored in Hadoop without needing programming skills.
This is a nice example of Kafka enablement using Maxwell (a mysql-to-kafka binlog processor) and StreamSets Data Collector from the folks at B23. It includes a schema change listener for handling data drift. Enjoy!
Innovate on Your Data – Maxwell Meets StreamSets
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