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Lambda Architecture For Big Data By @JnanDash | @ThingsExpo [#BigData #IoT]

Lambda Architecture is a useful framework to think about designing big data applications

I attended a Meetup yesterday in Mountain View, hosted by The Hive group on the subject of Lambda Architecture. Since I had never heard about this new phrase, my curiosity took me there. There was a panel discussion and panelists came from Hortonworks, Cloudera, MapR, Teradata, etc.

Lambda Architecture is a useful framework to think about designing big data applications. Nathan Marz designed this generic architecture addressing common requirements for big data based on his experience working on distributed data processing systems at Twitter. Some of the key requirements in building this architecture include:

  • Fault-tolerance against hardware failures and human errors
  • Support for a variety of use cases that include low latency querying as well as updates
  • Linear scale-out capabilities, meaning that throwing more machines at the problem should help with getting the job done
  • Extensibility so that the system is manageable and can accommodate newer features easily

The following pictures summarizes the framework.

Overview of the Lambda Architecture

The Lambda Architecture as seen in the picture has three major components.

  1. Batch layer that provides the following functionality
    1. managing the master dataset, an immutable, append-only set of raw data
    2. pre-computing arbitrary query functions, called batch views.
  2. Serving layer—This layer indexes the batch views so that they can be queried in ad hoc with low latency.
  3. Speed layer—This layer accommodates all requests that are subject to low latency requirements. Using fast and incremental algorithms, the speed layer deals with recent data only.

Criticism of lambda architecture has focused on its inherent complexity and its limiting influence. The batch and streaming sides each require a different code base that must be maintained and kept in sync so that processed data produces the same result from both paths. Yet attempting to abstract the code bases into a single framework puts many of the specialized tools in the batch and real-time ecosystems out of reach.

The panelists rambled on details without addressing real challenges on combining two very different approaches, thus compromising the benefits of stream with added latency of the batch world. However, there is merit to the thought process of unification of the two disparate worlds into a common framework. Real deployment will be the proof point. 

Read the original blog entry...

More Stories By Jnan Dash

Jnan Dash is Senior Advisor at EZShield Inc., Advisor at ScaleDB and Board Member at Compassites Software Solutions. He has lived in Silicon Valley since 1979. Formerly he was the Chief Strategy Officer (Consulting) at Curl Inc., before which he spent ten years at Oracle Corporation and was the Group Vice President, Systems Architecture and Technology till 2002. He was responsible for setting Oracle's core database and application server product directions and interacted with customers worldwide in translating future needs to product plans. Before that he spent 16 years at IBM. He blogs at http://jnandash.ulitzer.com.

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