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Entries in big-data (6)

Tuesday
Aug282012

Making Hadoop Run Faster

Making Hadoop Run Faster

One of the challenges in processing data is that the speed at which we can input data is quite often much faster than the speed at which we can process it. This problem becomes even more pronounced in the context of Big Data, where the volume of data keeps on growing, along with a corresponding need for more insights, and thus the need for more complex processing also increases.

Batch Processing to the Rescue

Hadoop was designed to deal with this challenge in the following ways:

1. Use a distributed file system: This enables us to spread the load and grow our system as needed.

2. Optimize for write speed: To enable fast writes the Hadoop architecture was designed so that writes are first logged, and then processed. This enables fairly fast write speeds.

3. Use batch processing (Map/Reduce) to balance the speed for the data feeds with the processing speed.

Batch Processing Challenges

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Thursday
May242012

Build your own twitter like real time analytics - a step by step guide

Major social networking platforms like Facebook and Twitter have developed their own architectures for handling the need for real-time analytics on huge amounts of data. However, not every company has the need or resources to build their own Twitter-like solution.

In this example we have taken the same Twitter/Facebook-like blueprint, and made it simple enough for developers to implement. We have taken the following approach in our implementation: 

  1. Use In Memory Data Grid (XAP) for handling the real time stream data-processing.
  2. BigData data-base (Cassandra) for storing the historical data and manage the trend analytics 
  3. Use Cloudify (cloudifysource.org)  for managing and automating the deployment on private or pubic cloud

The example demonstrate a simple case of word count analytics. It uses Spring Social to plug-in to real twitter feeds. The solution is designed to efficiently cope with getting and processing the large volume of tweets. First, we partition the tweets so that we can process them in parallel, but we have to decide on how to partition them efficiently. Partitioning by user might not be sufficiently balanced, therefore we decided to partition by the tweet ID, which we assume to be globally unique. Then we need persist and process the data with low latency, and for this we store the tweets in memory.

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Tuesday
Mar272012

Big Data In the Cloud Using Cloudify

Edd Dumbill wrote an interesting article on O’Reilly Radar covering the current solutions for running Big Data in the Cloud

Big data and cloud technology go hand-in-hand. Big data needs clusters of servers for processing, which clouds can readily provide.

Big PaaS

Edd touched briefly on the role of PaaS for delivering Big Data applications in the cloud

Beyond IaaS, several cloud services provide application layer support for big data work. Sometimes referred to as managed solutions, or platform as a service (PaaS), these services remove the need to ucale things such as databases or MapReduce, reducing your workload and maintenance burden. Additionally, PaaS providers can realize great efficiencies by hosting at the application level, and pass those savings on to the customer.

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Friday
Sep232011

The Real News is Not that Facebook Serves Up 1 Trillion Pages a Month…

It’s how much load that really generates and how it scales to meet the challenge.

imageThere’s some amount of debate whether Facebook really crossed over the one trillion page view per month threshold. While one report says it did, another respected firm says it did not; that its monthly page views are a mere 467 billion per month.

In the big scheme of things, the discrepancy is somewhat irrelevant, as neither show the true load on Facebook’s infrastructure – which is far more impressive a set of numbers than its externally measured “page view” metric.

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Tuesday
Sep062011

Big Data Application Platform

It's time to think of the architecture and application platforms surrounding "Big Data" databases. Big Data is often centered around new database technologies mostly from the emerging NoSQL world. The main challenge that these databases solve is how to handle massive amount of data at a reasonable cost and without poor performanc - distributed databases emerged to address this challenge and today we're seeing high adoption rate and quite impressive success stories such as the Netflix use of Cassandra/DataStax solution. All that indicate the speed in which this market evolves.

The need for a Big Data Application Platform

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Monday
Jul182011

Building your own Facebook Realtime Analytics System  

Recently, I was reading Todd Hoff's write-up on FaceBook real time analytics system. As usual, Todd did an excellent job in summarizing this video from Engineering Manager at Facebook Alex Himel.

In the first post, I’d like to summarize the case study, and consider some things that weren't mentioned in the summaries. This will lead to an architecture for building your own Realtime Time Analytics for Big-Data that might be easier to implement, using Facebook's experience as a starting point and guide as well as the experience gathered through a recent work with few of GigaSpaces customers. The second post provide a summary of that new approach as well as a pattern and a demo for building your own Real Time Analytics system..

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