Turn Your Data into a Strategic Asset – Big Data & Data Lake Experts

Build scalable, secure, AI-ready data lakes and lakehouses that deliver real-time insights and 10× faster decisions.

Get Your Free Data Lake Assessment
Transformation of structured data, semi-structured data and unstructured data into data lake

Is Unmanaged Data Slowing Your Business Down?

Most companies globaly struggle with:

  • Siloed data across departments
  • Slow, expensive reporting (days instead of minutes)
  • “Data swamp” instead of data lake”
  • High cloud bills with no ROI
  • Compliance risks and security gaps

You’re sitting on petabytes of gold — but can’t mine it.

We Build Enterprise-Grade, AI-Ready Data Lakes

All your data at one place. Zero headaches.

Store Everything

Raw files, logs, JSON, video, IoT at petabyte scale

Govern Once, Use Everywhere

Unified security, lineage & catalog

Query 10× Faster

With Apache Iceberg, Trino, Spark & Oracle Autonomous

Fuel AI & Analytics

Direct access for Data Science, BI, and GenAI

End-to-End Big Data & Data Lake Services

Data Lake Strategy & Roadmap

Custom architecture tailored to your industry

Cloud & On-Prem Data Lake Build

Oracle OCI ∙ AWS ∙ Azure ∙ Hybrid

Real-Time Ingestion & Streaming

Kafka ∙ Flink ∙ GoldenGate ∙ NiFi

Governance, Security & Compliance

Ranger ∙ Unity Catalog ∙ Fine-grained policies

Advanced Analytics & BI

Looker ∙ Power BI ∙ Oracle Analytics ∙ Custom Dashboards

AI/ML Integration & Model Deployment

Direct training on lake data – no copying

Powered by the Best-in-Class Ecosystem

Real Results for Pakistani & Global Companies

Leading Textile Group (Karachi)
  • 40 PB data lake on our Data Lake Solution
  • Query time reduced from 4 hours to <8 minutes
  • 34% supply-chain cost savings
Top Private Bank (Lahore)
  • Autonomous AI Lakehouse across OCI + on-prem
  • Real-time fraud detection
  • 99.7% accuracy improvement
E-commerce Unicorn (UAE + Pakistan)
  • Multi-cloud lakehouse (AWS + Azure)
  • 3.2 million daily events processed
  • 28% increase in conversion rate

Why ServIDEAS?

  • 15+ years specialized in big data (not generalist agency)
  • 10+ certified big data engineers
  • Fixed-price + outcome-based models
  • 24×7 Pakistani support team (no offshore handoff)
  • 60-day “Performance Guarantee” – or we fix it free

Frequently Asked Questions

  • A Data Warehouse is a structured repository optimized for analysis and reporting. It stores processed, cleaned, and transformed data in a schema-on-write approach (data must follow a predefined schema before being loaded). Typically used for business intelligence and SQL-based analytics.

  • A Data Lake is a centralized repository that stores raw data in its native format (structured, semi-structured, unstructured) at any scale. It uses a schema-on-read approach (schema is applied only when data is read). Ideal for big data analytics, machine learning, and advanced analytics on diverse data types (logs, JSON, images, videos, IoT data, etc.).

Organizations adopt Data Lakes because they:

  • Can store massive volumes of raw data cost-effectively (especially on cloud object storage like S3, Azure Data Lake Storage, or GCS).
  • Support all types of data (structured, semi-structured, unstructured) without upfront transformation.
  • Enable advanced use cases like machine learning, real-time analytics, log analysis, and data science exploration.
  • Provide flexibility — data scientists and engineers can apply different schemas depending on the use case.
  • Avoid data silos by acting as a single source of truth for the entire organization.

A Data Lakehouse is a modern architecture that combines the best of Data Lakes and Data Warehouses.

  • Traditional Data Lakes lack ACID transactions, governance, and strong performance on BI queries.
  • A Data Lakehouse adds warehouse-like features directly on top of the data lake using open table formats (Delta Lake, Apache Iceberg, Apache Hudi):
  • ACID transactions
  • Schema enforcement and evolution
  • Time travel and versioning
  • High-performance SQL queries
  • Data governance and quality

This eliminates the need for a separate data warehouse while keeping the flexibility and low cost of a data lake.

Common challenges include:

  • Data Swamp: Without governance, a data lake turns into an unusable “swamp” of undocumented, duplicate, and poor-quality data.
  • Lack of data cataloging and metadata management.
  • Security and access control (especially with sensitive data).
  • Ensuring data quality and lineage and compliance (GDPR, CCPA, etc.).
  • Performance issues when running BI queries directly on raw data.
  • High operational complexity in managing ingestion pipelines at scale.

These are usually addressed with tools like data catalogs (AWS Glue, Databricks Unity Catalog, Collibra), lakehouse formats, and governance frameworks.

Widely used modern Data Lake / Lakehouse platforms include:

  • Databricks Lakehouse (built on Delta Lake)
  • Snowflake (supports unstructured data and lakehouse features)
  • AWS (S3 + Glue + Athena + Lake Formation)
  • Azure Synapse Analytics + Azure Data Lake Storage Gen2 + Purview
  • Google Cloud (BigQuery + Cloud Storage + Dataplex)
  • Open-source table formats: Delta Lake, Apache Iceberg, Apache Hudi
  • Data catalog & governance: Unity Catalog, AWS Glue, Microsoft Purview, Alation, Collibra

These platforms have largely replaced the older Hadoop-based data lakes (HDFS + Hive) in enterprise environments.

Secure your Big Data

Ready to Build Your Future-Proof Data Lake?

Book Your Free 45-Min Assessment Call (No obligation – limited slots this month)


Book Free 45-min Project Rescue Call