Devops

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MiCPM™ is here, and MityLytics is excited to invite you to trial MiCPM for Spark clusters!

By | 2017-10-10T15:48:40+00:00 May 15th, 2017|Categories: Big Data Performance, Devops|Tags: , , , |

Thanks to our partners at Packet.net, MityLytics can offer you a test drive of MiCPM with Spark-HDFS clusters. MiCPM is a zero-touch, point and deploy SaaS platform that provides simulation and prediction capabilities for workloads and performance tuning at the touch of a button. With MiCPM, your clusters run more efficiently, reducing infrastructure TCO and increasing [...]

Reduce Costs by Improving Your Analytics Efficiency

By | 2017-10-10T15:49:33+00:00 March 30th, 2017|Categories: Big Data Performance, Devops|

According to IDC, companies are expected to be spending over $187B on Big Data and Business Analytics software by 2019. Approximately $55B of that is expected to be spent on software, with another $28B to be spent on hardware and the balance spent on IT services. With such large numbers, even incremental gains in efficiency can [...]

Performance Prediction and bottleneck identification for Spark, Hadoop and Hive

By | 2016-12-01T13:31:03+00:00 September 14th, 2016|Categories: Big Data Performance, Devops|

Characterize and predict performance, identify bottlenecks as you scale up your, do all that without actually spinning up your cluster. A way for you to correctly size your resources (CPU, RAM, Storage and Networking) as your dataset grows. Illustrated in this talk with benchmark suites. If interested drop us a line, we are working with a [...]

Keeping up with open source software for Big Data

By | 2016-12-01T13:31:28+00:00 November 5th, 2015|Categories: Big Data Performance, Devops|

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 [...]