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

From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data. Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our products for riders, …

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

Scaling Airbnb’s Experimentation Platform

At Airbnb, we are constantly iterating on the user experience and product features. This can include changes to the look and feel of the website or native apps, optimizations for our smart pricing and search ranking algorithms, or even targeting the right content and timing for our email campaigns. For the majority of this work, we leverage our internal A/B Testing platform, the Experimentation Reporting Framework (ERF), to validate our hypotheses and quantify the impact of our work. Read about the basics of ERF and our philosophy on interpreting experiments.

Introduced in 2014, ERF began as a tool that allowed users to define metrics in a common configuration language, and then compute and surface …

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Will SQL just die already?

With tons of new No-SQL database offerings everyday, developers & architects have a lot of options. Cassandra, Mongodb, Couchdb, Dynamodb & Firebase to name a few. Join 33,000 others and follow Sean Hull on twitter @hullsean. What’s more in the data warehouse space, you have Hadoop, which can churn through terabytes of data and get … Continue reading Will SQL just die already? →

Designing Euclid to Make Uber Engineering Marketing Savvy

Fast, granular, reliable ROI on ad performance was our bugle call to build Euclid, Uber’s in-house marketing platform. Early this year, Euclid replaced a legacy system, which processed ROI data somewhat manually as it struggled to keep up with Uber’s …

The post Designing Euclid to Make Uber Engineering Marketing Savvy appeared first on Uber Engineering Blog.

The Uber Engineering Tech Stack, Part II: The Edge and Beyond

Uber Engineering

Uber’s mission is transportation as reliable as running water, everywhere, for everyone. Last time, we talked about the foundation that powers Uber Engineering. Now, we’ll explore the parts of the stack that face riders and drivers, starting …

The post The Uber Engineering Tech Stack, Part II: The Edge and Beyond appeared first on Uber Engineering Blog.

How to Deploy a Cluster

 

In this blog post I will talk about how to deploy a cluster, the methods I tried and my solution to resolving the prerequisites problem.

I’m fairly new to the big data field. Learning about Hadoop, I kept hearing the term “clusters”, deploying a cluster, and installing some services on namenode, some on datanode and so on. I also heard about Cloudera manager which helps me to deploy services on my cluster, so I set up a VM and followed several tutorials including the Cloudera documentation to install cloudera manager. However, every time I reached the “cluster installation” step my installation failed. I later found out that there are several prerequisites for a Cloudera Manager Installation, which was the reason for the failure to install.

 

Deploy a Cluster

Though I discuss 3 other methods in detail, ultimately I recommend method …

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Log Buffer #428: A Carnival of the Vanities for DBAs

The Log Buffer Edition once again is sparkling with some gems, hand-picked from Oracle, SQL Server and MySQL.

Oracle:

  • Oracle GoldenGate 12.1.2.1.1  is now certified with Unity 14.10.  With this certification, customers can use Oracle GoldenGate to deliver data to Teradata Unity which can then automate the distribution of data to multiple Teradata databases.
  • How do I change DNS servers on Exadata storage servers.
  • Flushing Shared Pool Does Not Slow Its Growth.
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Using Apache Hadoop and Impala together with MySQL for data analysis

Apache Hadoop is commonly used for data analysis. It is fast for data loads and scalable. In a previous post I showed how to integrate MySQL with Hadoop. In this post I will show how to export a table from  MySQL to Hadoop, load the data to Cloudera Impala (columnar format) and run a reporting on top of that. For the examples below I will use the “ontime flight performance” data from my previous post (Increasing MySQL performance with parallel query execution). I’ve used the Cloudera Manager v.4 to install Apache Hadoop and Impala. For this test …

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Percona Live London 2013: an insider’s view of the schedule

With the close of call for papers earlier this month, the Percona Live London conference committee was in full swing this past week reviewing all of the many submissions for November’s Percona Live London MySQL Conference.

The submissions are far ranging and cover some really interesting topics, making the lineup for Percona Live London really strong! What the committee looks for in a submission is how much “value” a talk will bring to the conference – this is to say it needs to be far more that a product demo. As such, real-world experiences are receiving much more favorable reviews, along with talks that cover methodologies the attendees will …

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Big Data with MySQL and Hadoop at MySQL Connect 2013

I will be talking about Big Data with MySQL and Hadoop at MySQL Connect 2013 (Sept. 21-22) in San Francisco as well as at Percona University at Washington, DC (September 12, 2013). Apache Hadoop is a very popular Big Data solution and we can nowadays easily integrate it with MySQL. I will start with a brief introduction of Apache Hadoop and its components (HFDS, Map/Reduce, Hive, HBase/HCatalog, Flume, Scoop, etc). Next I will show 2 major Big Data scenarios:

  • From file to Hadoop to MySQL. This is an example of “ELT” process: Extract data from external source; Load data into Hadoop; Transform data/Analyze data; Extract results to MySQL. It is similar to the original Data Warehouse ETL …
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Showing entries 1 to 10 of 19
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