Databricks consulting

Spark is the standard for Big Data processing and Databricks, founded by the original creator of Spark, is the best place to Spark.

It is also the best place to run Delta Lake and MLFlow, their latest open source contribution. Delta Lake helps entreprises to bing the performances and the reliability of databases to their existing Data Lake. MLFlow helps entreprises manage their Machine Learning lifecycle, enabling Data Scientists to efficiently go from rawdata to Machine Learning models in one platform

DevOps lifecycle

Build a practice

Transform your Big Data practice, we work with your teams to build Databricks skills.

  • Accelerate the time to value
  • Expand the value proposition, for you Big Data & AI solutions

Build a unified Analytics practice, deliver Big Data & AI driven innovation by unifying

  • Data Science
  • Data Engineering
  • Business

DevOps lifecycle

Methodologie et qualification des besoins

  1. Qualify the use case

      what is the business challenge today
      what is the business outcome / value the client is hoping to achieve
  2. Qualify the data

      is the data in the cloud
      describe the data: type, size, format, speed, ...
      understand the complexity of the Big Data the client is working with
  3. Qualify the solution

      describe the current technology ecosystem and data pipeline architecture
      who are the data users? (data scientits, data engineers, business users)
DevOps lifecycle

First steps with the Databricks platform

  • Databricks pricing
  • Competitive analysis
  • architectural reviews
  • completing a POC
  • Common migration patterns
  • Project planning

Four business values

  1. Accelerate inovation
  2. Increase productivity and reduce delivery
  3. Reduce Big Data costs
  4. Make data more reliable and performant
DevOps lifecycle
Discover past work and don't recreate the wheel

Discover past work and don't recreate the wheel

  • Building models is a very iterative process and most gains are incremental
  • Almost all Data Scientist teams regularly recreate work and therefore won't get as far as they could by refining past work. It is also a waste of money.
Collaboration between DS

Collaboration between DSCollaboration between DS

  • There is value to also sharing past work or working together on diffrent parts of the problem. Having a system of record for how work is done makes things easier and increase satisfaction.
  • Collaborate with business users, data engineers, analyts

Easy reproducibility of own and other works

  • If a model is not reproducible, it is worthless
  • It is also a cornertone of collaboration. Two individuals need to be able to reproduce others results.

Articles related to Databricks

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We are a team of Open Source enthusiasts doing consulting in Big Data, Cloud, DevOps, Data Engineering, Data Science…

We provide our customers with accurate insights on how to leverage technologies to convert their use cases to projects in production, how to reduce their costs and increase the time to market.

If you enjoy reading our publications and have an interest in what we do, contact us and we will be thrilled to cooperate with you.