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Displaying posts with tag: cassandra (reset)
Databook: Turning Big Data into Knowledge with Metadata at Uber

Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.

The post Databook: Turning Big Data into Knowledge with Metadata at Uber appeared first on Uber Engineering Blog.

Pythian at DataOps Barcelona

DataOps Barcelona is an event organized by Binlogic and will be taking place June 21-22, 2018 in Barcelona, Spain. Pythian is proud to be supporting this inaugural open source-focused conference in Europe!

DataOps is of course short for “Database + Operations” and contains talks about the various open-source database ecosystems and how they are operationalized with regards to cloud solutions, security, automation, high availability and scaling.

Pythian has a long history of helping companies leverage open source technologies and cloud infrastructure to meet their business requirements. We excel at managing traditional open source relational database (MySQL) and relatively new NoSQL technologies (MongoDB, Cassandra).

Our …

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Cassandra on Fedora 27

The last time that I installed Cassandra was on a version of Fedora 20. So, I new the first thing to check was the installation of Java. You can check the Java installation with two statements on a fresh installation of Fedora 27. You need to check the Java runtime and then the Java Software Development Kit before installing, starting, and using Cassandra.

Installing Prerequisites

You check the Java runtime with this command:

java -version

It should return:

openjdk version "1.8.0_171"
OpenJDK Runtime Environment (build 1.8.0_171-b10)
OpenJDK 64-Bit Server VM (build 25.171-b10, mixed mode)

You check the Java Software Development Kit (JSDK) with this command:

javac -version

It should return:

javac 1.8.0_171

After verifying the Java and JSDK installation, you can install the Cassandra packages with the following yum command as the …

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On RDBMS, NoSQL and NewSQL databases. Interview with John Ryan

“The single most important lesson I’ve learned is to keep it simple. I find designers sometimes deliver over-complex, generic solutions that could (in theory) do anything, but in reality are remarkably difficult to operate, and often misunderstood.”–John Ryan

I have interviewed John Ryan, Data Warehouse Solution Architect (Director) at UBS.

RVZ

Q1. You are an experienced Data Warehouse architect, designer and developer. What are the main lessons you have learned in your career?

John Ryan: The single most important lesson I’ve learned is to keep it simple. I find designers sometimes deliver over-complex, generic solutions that could (in theory) do anything, but in reality are remarkably difficult to operate, and often misunderstood. I believe this stems from a lack of understanding of the …

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Continuent Road Map: One year after restart… Where next?

You may know Continuent Tungsten for our highly advanced MySQL replication tool, Tungsten Replicator, and for our state-of-the-art MySQL clustering solution, Tungsten Clustering. Our solutions are used by leading SaaS vendors, e-commerce, financial services and telco customers.

But there are more, many more, Tungsten deployments out there. Tungsten Replicator can be used for real-time data

Cassandra Query Language

After installing Cassandra and reading Cassandra The Definitive Guide, it struck me that I should learn a bit more about the Cassandra Query Language (CQL). So, after I setup a single-node environment and created a .bashcassandra environment file to connect as a student user to the Cassandra instance:

# Add the Java and JRE paths to the $PATH environments.
export set PATH=$PATH:/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.45.x86_64:/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.45.x86_64/jre

# Add the $JAVA_HOME and $JRE_HOME environment variables.
export set JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.45.x86_64/
export set JRE_HOME=/usr

Having started Cassandra as the cassandra user, I connected to the Cassandra Query Language Shell (cqlsh) …

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Tungsten Replicator and Clustering 5.2.0 Released

Continuent are pleased to announce the release of Tungsten Replicator and Tungsten Clustering 5.2.0

This release is one of our most exciting new releases for a while, as it contains some significant new features and lays the groundwork for some additional new functionality in the upcoming 5.3.0 and 6.0 releases due later this year.

In particular, this release includes the following new features:

  • New replicator filtering environment to make filtering quicker and easier to use, and more flexible
    • New filter configuration standard for new filters
    • New filter to make replication out of a cluster easier
    • New filters for filtering events and data
  • New applier for sending Apache Kafka messages directly from an incoming data stream
  • New applier for adding incoming records directly to Elasticsearch for indexing
  • New …
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Percona Live 2017: Hawkular Metrics, An Overview

The place is still frantic here at Percona Live 2017 as everybody tries to squeeze in a few more talks before the end of the day. One such talk was given by Red Hat’s Stefan Negrea on Hawkular Metrics.

Hawkular Metrics is a scalable, long-term, high-performance storage engine for metric data. The session was an overview of the project that includes the history of the project, an overview of the Hawkular ecosystem, technical details of the project, developer features and APIs and third party integrations.

Hawkular Metrics is backed by Cassandra for scalability. Hawkular Metrics is used and exposed by Hawkular Services.The API uses JSON to communicate with clients.

Users of Hawkular Metrics include: …

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What engineering roles are most in demand at startups?

via GIPHY I was just reading over StackOverflow’s 2017 Developer survey. As it turns out there were some surprising findings. Join 33,000 others and follow Sean Hull on twitter @hullsean. One that stood out was databases. In the media, one hears more and more about NoSQL databases like Cassandra, Dynamo & Firebase. Despite all that … Continue reading What engineering roles are most in demand at startups? →

Netflix Data Benchmark: Benchmarking Cloud Data Stores

The Netflix member experience is offered to 83+ million global members, and delivered using thousands of microservices. These services are owned by multiple teams, each having their own build and release lifecycles, generating a variety of data that is stored in different types of data store systems. The Cloud Database Engineering (CDE) team manages those data store systems, so we run benchmarks to validate updates to these systems, perform capacity planning, and test our cloud instances with multiple workloads and under different failure scenarios. We were also interested in a tool that could evaluate and compare new data store systems as they appear in the market or in the open source domain, determine their performance characteristics and limitations, and gauge whether they could be used in production for relevant use cases. For these purposes, we wrote Netflix Data Benchmark

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