Databricks Consulting and Implementation

graphic property of Databricks

Big Data requires a

Big Data Lakehouse

By combining the best in Data Warehouses and Data Lakes, the Databricks Data Lakehouse offers speed, power, and flexibility.

Housing both structured and unstructured data, your data lakehouse can provide the access you need to put your data to work for you.

Let’s talk about Databricks!

What is a Data Lakehouse?

To understand the meaning of the term “Data Lakehouse”, we need to first review the other storage options.

Data Warehouses became popular in the 1980’s as a means for businesses to store data for specific uses.  Data had to be structured and mapped for an intended purpose.

The Big Data of the early 2000’s, and the desire for AI and ML applications, made Data Warehouses difficult to manage.  Data Lakes were formed to house unstructured data alongside the structured data.  This unstructured data storage provided the opportunity for Data Scientists to tap into more ways to gain insights into the vast amounts of data organizations were generating, like images, videos, and more.  Unfortunately, the unstructured data led to complications which brought about the term “data swamps”.

Today’s data engineers and architects needed a hybrid approach to managing the data.  That is the idea behind the Data Lakehouse. Data Lakehouses apply metadata, cataloging, and indexing to unstructured data stored in cloud blob storage. This enables scaling compute and storage independently of each other, distributing data processing across a cluster, ACID compliant-transactions, support for SQL, Python, and Scala. The result being faster, more seamless accessibility for algorithms using computer vision, natural language processing, and other technologies.

Data Lakehouses are also more economical; incoming data is automatically integrated, fewer systems would need to be accessed, and data prep time is shorter.

Manage your data in new ways

With a new, deeper access into your data, the possibilities are endless.

Data stored on one platform means the elimination of data silo disconnects.  Implementation of new AI processes are faster and more efficient.  Databricks Lakehouse works with your Azure and AWS storage environments

Accessibility

Transition to cloud storage with Databricks Data Lakehouse architecture

Implementation

Custom ML and AI solutions built to address your specific needs with the efficiencies of the Databricks Lakehouse

Actionable Insights

Best-practice AI/ML solutions provide deeper views into your data for informed decision making and projections

Not sure where to start?

Consider an Assessment

NLP Logix is a diverse team of Software Engineers, Data Scientists, Mathematicians, and Statisticians experienced in custom AI/ML and Deep Learning solutions to provide optimal insight into your data

We are proud to partner with Databricks to provide the consulting and implementation you need.