AI, Data & Intelligence
Data Lakes
Building scalable cloud data lakes on AWS S3, Azure Data Lake and Google Cloud Storage for structured and unstructured data.
Capability overview
What data lakes involves
Data lakes provide centralized, low-cost cloud storage for massive volumes of structured, semi-structured and unstructured enterprise data. We build scalable cloud data lakes on AWS S3, Azure Data Lake Storage (ADLS Gen2) and Google Cloud Storage.
We structure data lake zones (Raw/Bronze, Cleansed/Silver, Curated/Gold), optimizing storage formats (Parquet, ORC, Avro) and partition strategies.
Our data lake engineers implement data catalogs, access control policies and query engine integrations, enabling fast ad-hoc analysis and data science exploration.
During the data lakes engagement, our specialists work closely with your technical leads to establish tailored operational workflows, automated validation controls and clear deliverables for multi-tier data lake architecture and file format & partition optimization. From initial data ingestion audit through to lake architecture design, we embed continuous telemetry monitoring, structured documentation and risk mitigation rules tailored specifically for your organization's data lakes goals and data cataloging & metadata setup requirements.
What is included
What the engagement covers
Multi-Tier Data Lake Architecture
Designing Raw (Bronze), Cleansed (Silver) and Curated (Gold) storage layer structures.
File Format & Partition Optimization
Converting raw files to columnar Parquet/ORC formats and optimizing directory partitioning strategies.
Data Cataloging & Metadata Setup
Integrating AWS Glue Data Catalog, Azure Purview or Apache Atlas for automated metadata indexing.
Access Control & Encryption
Configuring IAM policies, bucket encryption (KMS) and fine-grained data access security rules.
How we work
How we deliver data lakes
Data Ingestion Audit
Analyzing data sources, file formats, ingestion frequencies and data lake storage scale expectations.
Lake Architecture Design
Designing storage folder structures, file format standards, lifecycle policies and partition schemes.
Ingestion Pipeline Engineering
Building automated ingestion scripts loading data from databases, APIs, IoT streams and flat files into the lake.
Catalog & Query Setup
Configuring Glue Crawlers, Athena, Databricks or Starburst Presto for fast SQL querying over lake files.
Security & Compliance Hardening
Applying bucket encryption, access logging and IAM roles to meet enterprise security standards.
Related capabilities
Related capabilities in Data Engineering & Platforms
Data Lakehouse Architecture
Building modern data lakehouses using Databricks Delta Lake, Apache Iceberg and Snowflake for real-time analytics and AI.
Data Integration
Connecting disparate enterprise applications, databases, SaaS platforms and cloud systems into unified data pipelines.
Real-Time Data Processing
Engineering low-latency streaming data pipelines, event processing engines and real-time analytical dashboards.
Data Migration
Legacy data migration services, cloud database migration, database schema translation and zero-downtime data cutover.
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Questions & answers
Questions about Data Lakes
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionParquet is a columnar file format that drastically reduces storage space through compression and accelerates SQL query speeds by reading only requested columns.
Data lake development is quoted as a fixed-fee implementation project based on data source count, storage volume and catalog scope.
By enforcing strict file naming rules, automated metadata cataloging, data quality checks and clear multi-tier storage zone separation.
Next step
Discuss data lakes with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.