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Data Lakehouse

Explore data’s infinite potential with an architecture that flexibly supports your needs. With a unified data lakehouse built on open standards, you can pursue BI, AI, and compliance use cases more effectively.

Data Lakehouse Banner

Data Lakehouse: Features and Benefits

Data lakehouse builds on the core concept of a data lake and gives it the structure, performance, and governance capabilities of a data warehouse. By combining the strengths of both systems, the data lakehouse offers a way out of the data silos, latency, and lineage issues that afflict traditional data management.

Data Lakehouse Diagram

Scalable Data Storage

A data lakehouse uses scalable object storage systems like Amazon S3, Azure Blob Storage, and Google Cloud Storage, just like a data lake. Vast amounts of structured, unstructured, and semistructured data can be stored in the lakehouse.

Strong Data Management

Schemas can be enforced to improve data consistency and discoverability. Data validation rules and cleansing pipelines help address data integrity issues and ensure compliance with business logic. Governance is enhanced through fine-grained access controls and audit mechanisms.

ACID Guarantee

Transaction engines like Delta Lake and Apache Iceberg provide strong ACID guarantees for applications that require them. These engines help the data lakehouse overcome the limitations of traditional data lakes, particularly their inability to handle concurrent read and write operations reliably.

End-to-End Streaming

Data can be analyzed as it is generated for real-time insights into trends, patterns, and anomalies. By supporting end-to-end streaming, a data lakehouse eliminates the need for a separate system to serve real-time data applications.

Open Environment

Standard open file formats (Parquet, ORC, and Avro) along with APIs facilitate direct access to BI tools and machine learning frameworks. This helps lower costs associated with ETL and reduces data duplication across different systems.

Decoupled Storage and Compute

Compute resources can be scaled up or down without impacting the underlying storage. This ensures consistent performance even if the data volume and query complexity increase. Also, you only need to pay for the resources being used.

Scalable
Data Storage

A data lakehouse uses scalable object storage systems like Amazon S3, Azure Blob Storage, and Google Cloud Storage, just like a data lake. Vast amounts of structured, unstructured, and semistructured data can be stored in the lakehouse.

Strong
Data Management

Schemas can be enforced to improve data consistency and discoverability. Data validation rules and cleansing pipelines help address data integrity issues and ensure compliance with business logic. Governance is enhanced through fine-grained access controls and audit mechanisms.

ACID
Guarantee

Transaction engines like Delta Lake and Apache Iceberg provide strong ACID guarantees for applications that require them. These engines help the data lakehouse overcome the limitations of traditional data lakes, particularly their inability to handle concurrent read and write operations reliably.

End-to-End
Streaming

Data can be analyzed as it is generated for real-time insights into trends, patterns, and anomalies. By supporting end-to-end streaming, a data lakehouse eliminates the need for a separate system to serve real-time data applications.

Open
Environment

Standard open file formats (Parquet, ORC, and Avro) along with APIs facilitate direct access to BI tools and machine learning frameworks. This helps lower costs associated with ETL and reduces data duplication across different systems.

Decoupled Storage
and Compute

Compute resources can be scaled up or down without impacting the underlying storage. This ensures consistent performance even if the data volume and query complexity increase. Also, you only need to pay for the resources being used.

Data Lakehouse Architecture

A multi-layered lakehouse architecture enables flexible exploratory analysis while also addressing specific analytical requirements. Data is organized in three layers: bronze, silver, and gold. Raw data is ingested into the bronze layer, which also serves as a data archive. In the silver layer, the raw data is transformed, personally identifiable information (PII) is removed, and a single, standardized view of the data is made available for data scientists and analysts. The data is further refined and curated in the gold layer for business-specific applications. Such a layered approach not only simplifies data management and access but also helps improve governance and compliance.

Architecture Diagram
Logos

Build a Strong Foundation for Your Data with QBurst

If your organization’s data volume, variety, and velocity have exploded in recent years, adopting a data lakehouse architecture can simplify its management and accelerate your time to value.

Design

Design

With more than a decade’s experience architecting and implementing enterprise-grade solutions for our clients, our data engineering team is equipped to deal with ground realities and technological trade-offs while selecting tools while designing your data lakehouse.

Implementation

Implementation

Our deep engineering expertise comes in handy while designing ETL/ELT pipelines, integrating data from disparate sources, implementing governance frameworks, and optimizing query execution. This helps you avoid common pitfalls that could be costly down the line.

Support

Ongoing Support

We design data solutions with an eye on their future scalability and adaptability so that your investment continues to pay forward as your data use cases evolve. Our ongoing support takes care of a smooth transition and timely updates help maintain the efficiency and effectiveness of your new systems.

Design Design

With more than a decade’s experience architecting and implementing enterprise-grade solutions for our clients, our data engineering team is equipped to deal with ground realities and technological trade-offs while selecting tools while designing your data lakehouse.

Implementation Implementation

Our deep engineering expertise comes in handy while designing ETL/ELT pipelines, integrating data from disparate sources, implementing governance frameworks, and optimizing query execution. This helps you avoid common pitfalls that could be costly down the line.

Support Ongoing Support

We design data solutions with an eye on their future scalability and adaptability so that your investment continues to pay forward as your data use cases evolve. Our ongoing support takes care of a smooth transition and timely updates help maintain the efficiency and effectiveness of your new systems.

Focus Areas for Transition to Data Lakehouse

Quality and Performance

By implementing robust data validation and transformation pipelines, we ensure that the errors in raw data do not creep into your insights. Engineering strategies such as data partitioning, caching, and tuning query execution help us prevent performance bottlenecks and improve lakehouse efficiency.

Cost Management

We mitigate the risk of cost overruns by implementing cloud cost management strategies such as auto-scaling, automated monitoring, and optimizations based on usage patterns. Following FinOps best practices throughout the CI/CD process helps us further balance the costs.

Phased Migration

Based on a mutually agreed roadmap, we help you migrate your workloads to a lakehouse structure and ensure seamless connectivity with other enterprise systems and applications. Migration is carried out in phases to assess impact, address issues early, and minimize disruption to operations.

Quality
and Performance

By implementing robust data validation and transformation pipelines, we ensure that the errors in raw data do not creep into your insights. Engineering strategies such as data partitioning, caching, and tuning query execution help us prevent performance bottlenecks and improve lakehouse efficiency.

Cost
Management

We mitigate the risk of cost overruns by implementing cloud cost management strategies such as auto-scaling, automated monitoring, and optimizations based on usage patterns. Following FinOps best practices throughout the CI/CD process helps us further balance the costs.

Phased
Migration

Based on a mutually agreed roadmap, we help you migrate your workloads to a lakehouse structure and ensure seamless connectivity with other enterprise systems and applications. Migration is carried out in phases to assess impact, address issues early, and minimize disruption to operations.

Frequently Asked Questions

What are the best use cases of data warehouses, data lakes, and data lakehouses?
  • Data Warehouse: Best suited for stable, well-defined analytical use cases. It can be adapted for unstructured data but is costly to scale.
  • Data Lake: Inexpensive and scalable storage for all types of data (relational, text, audio, video). With additional tooling, it can be adapted for processing and analytics.
  • Data Lakehouse: Suitable for large-scale data processing, analytics, and machine learning. Most efficient model for insights from diverse data types.
Should I migrate my data management to a lakehouse?

It is not an easy decision! But here are a few points for you to brainstorm:

Are you a data-hungry business? And are your use cases expanding beyond what your current systems can handle? If you are an e-commerce business, financial services company, or an organization with a data-first mindset, it pays to invest in a technology that matches your aspirations. And, at present, data lakehouses epitomize what is best in large-scale data management and analytics.

Let’s talk money. Integration challenges, specialized maintenance, productivity loss, and compliance and security risks can push up the cost of managing legacy data infrastructures. If cost optimization in the long term is a concern, you should start looking into the cost benefits of a lakehouse and consider an incremental migration.

Do you wish for a simplified architecture that also gives you greater flexibility while maintaining greater oversight? Evolving requirements and ad hoc decisions may have led to a disorganized infrastructure in your organization, but if managing it is a nightmare, then you can truly benefit from a well-structured data lakehouse.

Vendor lock-in is a legitimate fear, whether you are a start-up raring to go or an established enterprise. Limited flexibility and higher operational costs can clip your wings and cause your innovative projects to drag. An open lakehouse offers you greater freedom to choose tools and frameworks that best support your use case.

What are the risks of sticking with our existing architecture versus adopting a data lakehouse?

If your data needs have changed and it takes considerable shoehorning to meet them using existing systems, then there is a significant opportunity cost. Data is still the most valuable commodity out there and compromises can be costly both in the near and long term.

Switching to a lakehouse environment does involve substantial investment but cost efficiencies gained over time could balance the upfront cost. The benefits can be direct (in the form of optimal resource utilization) and indirect (in the form of better compliance enforcement).

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