Data Lakes

Scalable Storage for Every Data Type —
Governed and Analytics-Ready

As data volumes grow and source systems diversify — spanning IoT sensors, application logs, API feeds, social data, and unstructured documents — traditional warehouse architectures become insufficient to capture and retain the full breadth of organizational data. A data lake provides the centralized, scalable storage layer that ingests data in its native form, preserves it at low cost, and makes it available for a wide spectrum of analytical and machine learning workloads.

Contact TecBrix

Raw

Cleansed

Curated

JSON

XML

Parquet

Delta

ORC

TecBrix designs and implements production-grade data lake architectures that avoid the governance failures of unmanaged implementations — ensuring your lake remains navigable, trusted, and analytically useful rather than becoming an ungoverned data swamp.

What Is a
Data Lake?

A data lake is a centralized repository that stores raw, unprocessed data at scale — structured relational data, semi-structured formats such as JSON and XML, and unstructured content including text files, images, and binary objects — without requiring schema definition at ingestion time. This schema-on-read approach enables maximum ingestion flexibility and supports diverse downstream use cases including SQL analytics, Python-based data science, and large-scale machine learning training.

structured relational data

{ }

semi-structured formats such as JSON and XML

unstructured content including text files, images, and binary objects

Leading platforms include Amazon S3 with AWS Lake Formation, Azure Data Lake Storage Gen2 with Microsoft Purview, and Google Cloud Storage with Dataplex — each providing native integration with their respective analytics and AI services.

Amazon S3

AWS Lake Formation

Azure Data Lake Storage Gen2

Microsoft Purview

Google Cloud Storage

Dataplex

Common Challenges
TecBrix Resolves

Uncontrolled data ingestion creating an unnavigable repository with no clear ownership or lineage

Absence of access controls leading to regulatory and data privacy exposure

Inconsistent file formats, naming conventions, and partitioning strategies degrading query performance

Raw data not curated or modeled for consumption by BI tools and reporting layers

Machine learning teams unable to locate, trust, or version datasets reliably

How TecBrix
Helps

Lake Architecture — Raw to Curated Zones

Raw

Cleansed

Curated

TecBrix implements a multi-zone lake architecture — typically Raw, Cleansed, and Curated — with clearly defined data flows between zones, transformation logic applied at each stage, and consumption patterns documented for downstream teams. This zone-based approach ensures that raw source data is preserved for reprocessing, while curated datasets are reliable, performant, and accessible to BI and analytics consumers.

Partitioning strategies, file format standardization (Parquet, Delta, ORC), and compaction policies are defined at the architectural stage to ensure query engines operate efficiently against the stored data.

Parquet

Delta

ORC

Governance, Security & Access Control

Ungoverned lakes create significant compliance and security risk. TecBrix implements role-based and attribute-based access control at the storage and catalog level, integrating with enterprise identity providers. Data cataloging — using AWS Glue Data Catalog, Microsoft Purview, or Apache Atlas — provides discoverability, lineage tracking, and metadata management so users can locate, understand, and trust the data they consume.

Lifecycle management policies are configured to tier infrequently accessed data to lower-cost storage classes automatically, controlling long-term cost without manual intervention.

AWS Glue Data Catalog

Microsoft Purview

Apache Atlas

Analytics & AI Enablement

A data lake is not an end-state — it is a foundation. TecBrix ensures your curated datasets are consumable by SQL query engines (Athena, Synapse Serverless, BigQuery), BI platforms, and machine learning frameworks. We configure compute layers, optimize dataset structure for analytical queries, and document dataset schemas to support downstream teams working independently.

Athena

Synapse Serverless

BigQuery