Supercharge your
Data Lakes Architecture
With the Power of AI

Inzata offers self-organizing data lakes, powered by sophisticated AI Algorithms that process, organize, structure and clean all of your data - automatically upon ingest. If you're considering a data lake vs. data warehouse, read on to get the best of both.

See Inzata In Action


A sound data lake architecture is great for capturing vast amounts of structured and unstructured data, and for exploring data company-wide. But your data lake’s clear waters can become murky if the data isn’t cleaned and organized. If you’re using Amazon or Azure data lakes, conflicting definitions, duplicate values, orphaned bits of gibberish, and disconnected data can prevent your users from seeing below the surface to find the data they need. Use the power and scale of Inzata’s AI to auto-structure and organize your data lake to keep data flowing effectively from source to user. If you’re considering a data lake vs. data warehouse decision, ensure your data lakes organization with powerful and fast AI tools from Inzata.

  • Automatically clean data upon ingest – keep bad data out.
  • Auto-generate data dictionaries and profiling logs, help users locate the data they need without endless searching.
  • Connect any existing data lakes: Azure data lakes, Hadoop data lakes, Cloudera data lakes.
data lakes vs. data warehouse. data lake architecture
data analysis products

Automatically Give Structure to Data Lakes with AI Modeling


Data lakes are tremendously scalable and flexible, accommodating all types of data—and lots of it. Data lakes can help organizations make better use of their data more quickly, affordably, and at scale. The difference between a data lake vs. data warehouse is clear, data lakes are for bringing data together,  structured and unstructured data, all in one place.

But if the incoming data lacks organization and context, lakes can quickly start to look like disorganized dumping grounds, and lose all of their perceived value. Organizations can easily lose track of data, its context, and its meaning.

You want a solution that is self-organizing and self-managing. Only Inzata automatically profiles, cleans and “attaches” new data to the existing structures when it comes in. New data sources become instantly available for users to explore. Existing data is updated from source automatically. Don’t let your data lake become a data swamp. Keep it organized and managed with Inzata.


Data Lakes: Strike a Balance Between Agility and Control

In the past, if you wanted agility, you had to give up control and vice versa. How can agility and self-service be achieved if you’re also enforcing rules to make the data lake a fully governed environment?

Inzata leverages the compute power of the cloud data lake to also manage the data lake itself (by using AI, statistics, and machine learning to automate development, performance & management), data lakes can deliver both agility and governability.

You want to spend your time including new data sources for your lake, not endlessly managing the ones already there. Inzata’s Data Lake Software handles all of the low-level management for you, automatically. Your data lake becomes a dynamic tool that can drive innovation when data is easy to find, understand, and trust.

azure data lakes

Data Quality & Cleansing
Data Profiling

Nobody likes manually cleaning data. So we built AI to do it.

Inzata provides intuitive, visual and interactive data analysis software for business users to onboard, profile, and curate quality information. Inzata’s AI instantly scans every field in new datasets and gives you a statistical grade of its quality, column by column, helping you pinpoint problem areas and quickly clean them up.

Whether it’s duplicate records, missing fields, standardization, or structural issues, Inzata’s data analysis software engine instantly alerts you to problem areas and gives you a roadmap for correcting them, far faster than human data stewards. Clean data from Inzata can also be exported and re-uploaded to source systems or other data analysis products to correct problems at the source.



Organizations are adopting data lake designs because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics.

A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data.


  • Do you know what’s in your data lake?
  • Do you know what should be in your data lake?
  • Do you know where your data came from?
  • Do you know who has access to what data?
  • Do people use your data lake–and do they collaborate?
  • Have you reviewed your data lake architecture?
azure data lakes

Organize, Prep, and Clean
incoming Data with AI

Automated AI data profiling, preparation, and

Integrate, blend, and load data into visual business
models with ease

Instant joins, fuzzy-matching, and automatically fix
data quality problems

Big Data analysis software brought together with
powerful AI

Enterprise Scale,
Hyper Agility For Data Lakes

Enterprise-grade data engine & file based data
repository, built with OpenCL,  REST API and featuring Apache Spark and Kafka.

Supercharge your data lake architecture

Columnar storage structure, In-memory execution,
smart micro-caching for ultra-high performance in
public or private cloud

Connect Azure data lakes

Connect Hadoop data lakes

Inzata data analysis products are available as Software-as-a-Service, in a virtual private cloud or as an on-premises

Smart Insights
and Visuals

Create intuitive and engaging visuals for any
audience. Let your data tell its story.

Interactive dashboards reveal answers to
questions, hidden trends, and make your business
more data-driven.