Keeping up with open source software for Big Data

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Keeping up with open source software for Big Data

Hello from MityLytics again!

Have been meeting a lot of people involved in the analytics space and one thing stands out, the field is definitely evolving at breakneck pace with various degrees of backward compatibility. Trends seem to catch on and then just as quickly disappear or become obsolete or need to be plugged into something else. How do you protect yourself against this kind of volatility? How do you mesh together all these different platforms and infrastructure elements in a rapidly evolving enterprise where you are balancing the cloud, open-source freemium code and where Big Data distribution vendors assume you have unbounded resources, both in terms of manpower and compute/networking/storage? It is an unenviable position to be in but we are here to help in many different ways.

  1. Help you choose the right Big Data plumbing to connect? ingestion, analytics and storage
  2. Help you right-size your infrastructure? Avoid contention, even if you have unbounded resources you will still have contention with today’s distributions.
  3. Help you in making your deployment scale to accommodate bursts as well as long-term capacity expansion.
  4. Integrate IT silos for unified  analytics driven insights into your Big data  clusters whether in the cloud or in-house or in a colo.
  5. Model and Simulate your Big Data workloads in dev and stage environments to verify and validate your fixes and avoid the cycle of Deploy →Found problem in Production →  Rollback → Try again …
By | 2016-12-01T13:31:28+00:00 November 5th, 2015|Categories: Big Data Performance, Devops|0 Comments

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