DataLakeHouse is the opinionated open source stack for the modern data value chain. It incorporates best practices from infrastructure to data lake storage and ML to Analytics. It’s based on an idea of taking subject matter experts and data scientist to task to define convenient end-to-end processes for data ingestion, data wrangling, machine learning process flows, and reporting/analytics capabilities supported by best practice data pipelines to enable a myriad of business functions.

The DataLakeHouse is a solution stack based on a proven framework built using core open source technologies and supporting other leading solutions such as cloud data warehousing, KubeFlow, Airflow, notebooks, and other critical ML/Analytics infrastructure for Big and Small Data. This includes support for Apache Spark and MapReduce.  Thus it’s a unified data analytics platform that is slanted towards immediate business value.

The fact is that not every company has the in-house expertise, creative free-range, or skillsets to have a bleeding edge tech infrastructure like AirBnB, Uber, Netflix, Ebay, (insert other amazing groundbreaking tech team here), so we’ve taken similar (or in some cases the same) real-world situations that called for real-world solutions, and we’ve integrated them together. Now any business, no matter how small can benefit from not only Big Data lessons learned for value-add data pipeline outputs, but infrastructure, and DevOps/MLOps practices as well. Cloud technology reduces the barrier to entry to enable a data-driven culture and data-driven decision making for your business. It shouldn’t be complicated, so we’ve made it less complicated, for everyone.

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