a day ago
What Companies Should Know About Implementing A Data Lakehouse
Abhik Sengupta, Principal Solution Architect, Hakkoda.
Traditional data warehouses—once the backbone of business intelligence and reporting—are increasingly misaligned with today's data demands. The surge in data volume, velocity and variety has exposed their architectural constraints: rigid schemas, high storage costs, poor handling of semi-structured data, and reliance on batch-oriented extract, transform and load (ETL) processes.
In a Teradata/Vanson Bourne survey from 2018, 74% of decision-makers were already citing analytics complexity as a challenge with data warehouses, and 79% said users lacked access to all the data they needed. By 2021, DBTA reported that 88% of organizations struggled with data loading in these environments, and 42% still relied on manual cleanup and transformation.
These limitations are particularly problematic in cloud-native environments where real-time analytics, AI workloads and globally distributed teams demand flexibility and speed.
To overcome these challenges, many enterprises are adopting lakehouse architectures, which are intended to unify the governance and performance of data warehouses with the scalability and openness of data lakes.
As a principal solution architect, I've led several large-scale lakehouse implementations across platforms like Snowflake, Coalesce and Sigma. In this article, I'll explain how lakehouses can address legacy bottlenecks and what organizations should consider when modernizing their data platforms with this approach.
Why Companies Are Shifting To Lakehouse Architectures
At its core, a lakehouse stores structured, semi-structured and unstructured data in low-cost object storage while layering on transactional features, schema enforcement and version control through a metadata management layer. This enables organizations to build both batch and streaming data pipelines, maintain high data quality and support time travel and auditability within the same platform.
A major advantage of lakehouses is their interoperability. Multiple analytics and machine learning engines can access the same datasets simultaneously, eliminating the need for redundant copies or specialized infrastructure. This can improve collaboration across teams, speed up experimentation and simplify data governance.
By unifying ingestion, processing, analytics and AI workloads, lakehouses can reduce operational complexity while increasing agility. They can also provide a composable foundation for building domain-driven data products, enabling real-time personalization.
In fact, a study published earlier this year in Information Systems found that the lakehouse is "inexpensive, quick and adaptable" like a data lake, while combining the "structure and simplicity of a [data warehouse] with the broader use cases of a [data lake]."
From personal experience, in one project I worked on, onboarding time dropped by 40% due to reusable pipeline templates and declarative schema handling.
Importantly, built-in features like versioning and time travel enable data auditability, governance and lineage tracking using tools such as Great Expectations and CloudWatch. That said, it's important to consider which engines—such as Spark, Snowflake and Athena—are supported to enable flexible, future-ready analytics environments.
This will be particularly important as companies work to adopt AI. Unlike traditional data warehouses, lakehouses support diverse, large-scale datasets—including unstructured formats—within one repository. Versioning and snapshotting enable repeatable, auditable ML workflows. Support for Spark and Flink can allow scalable model training directly on fresh data, essential for real-time personalization and AI governance.
Technical Architecture: Building A Real-World Lakehouse Stack
Implementing a lakehouse architecture is a multiphase transformation that spans the full data life cycle, from ingestion to governance. It's not a one-size-fits-all deployment, but a set of strategic choices that must align with organizational priorities, technical maturity and interoperability needs:
1. Ingestion: This is the foundation, where teams must assess the nature of their data sources, expected latency and format diversity. Successful implementations typically use schema-aware tools that preserve metadata and support both batch and streaming pipelines to ensure consistency downstream.
2. Processing And Transformation: In this stage, raw data is converted into analytics- and ML-ready formats. Most lakehouse platforms support schema evolution, versioning and time-travel-like capabilities, allowing teams to build reproducible pipelines and accommodate changing data structures without data loss.
3. Implementing The Storage Layer: This typically uses cloud-native object stores (like S3, ADLS or GCS), with an open format and a metadata layer to manage immutability, partitioning and optimization. The goal is scalable, low-cost storage that enables fast access and governance at scale.
4. Query And Analytics: Lakehouses often support multi-engine interoperability, allowing business intelligence tools, SQL engines and ML frameworks to access the same governed datasets. Companies must catalog integration data and metadata consistently to ensure reliable performance and trusted insights.
5. Orchestration: Layers must accommodate schema evolution, rollback and modular pipelines. Most teams implement CI/CD for data workflows, using orchestration tools like Airflow, Dbt or Step Functions to ensure reproducibility and resilience.
6. Governance And Observability: Both of these functions should span the entire stack. Versioned metadata, data contracts, lineage tracking and quality testing tools (e.g., Great Expectations, Soda or Monte Carlo) play a central role in building trust and compliance across domains.
What It Takes To Prepare For The Lakehouse
Success with a lakehouse depends on more than just tooling—it requires team readiness, clear processes and thoughtful design. Organizations must build capabilities in schema evolution, cross-engine interoperability and performance tuning to meet latency and cost goals.
For compliance (e.g., GDPR, HIPAA, SOX), the architecture must support data lineage, time-based audits and immutability. This includes implementing version-controlled metadata, retention policies, role- and policy-based access controls, encryption (at rest and in transit) and detailed logging. Observability and data contracts are essential to detect quality issues before they become compliance risks.
Operationally, automation is key. Tasks like compaction, metadata cleanup and performance optimization must be built into workflows. While platform integration is improving, gaps remain in business intelligence and orchestration tools, making testing and validation critical.
Finally, readiness also depends on people. Invest in upskilling through structured training, reusable frameworks and real-world pilots. These accelerate adoption and reduce errors.
By addressing these concerns, companies can build a scalable lakehouse foundation—ready to support governed, high-performing data products and AI at enterprise scale.
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