As enterprises continue to struggle with exponentially increasing sources and amounts of data, the need for data lakehouse grows. A data lakehouse combines the scalability of a data lake with the performance and reliability of a data warehouse, bringing flexibility, cost efficiency, and faster analytics to the table.
The catch is the adoption of a lakehouse, where many organizations stumble. In this article, we discuss five common mistakes enterprises make during lakehouse adoption and how to avoid them effectively.
1. Failing to Optimize Storage and Compute Separation
One common mistake enterprises make with a lakehouse is not properly separating storage from compute. A data lakehouse allows scalable storage and flexible compute resources, but many organizations strictly tie compute resources to storage or fail to configure them efficiently. Consequently, they suffer higher costs, slower query performance, and wasted resources.
The best way to approach a lakehouse architecture is to scale compute and storage independently. This way, analytics workloads can run faster, large datasets can be stored cost-effectively, and teams can optimize performance for different use cases. Ignore this, and a promising lakehouse can turn from being a productive solution into a bottleneck.
2. Underestimating Data Governance and Security Needs
A data lakehouse comes with the promise of easy access to all enterprise data, but without governance, the promise might not materialize. There’s also an assumption among many organizations that a single platform automatically resolves governance issues, but that’s not the case. In reality, poorly defined access controls, inconsistent metadata management, and unclear data lineage breed compliance risks and erode trust in the platform.
Security is another critical facet. Encryption, role-based access, and auditing often take a backseat when migrating to a lakehouse. Regulatory compliance, especially in industries like finance, healthcare, and retail, demands that governance and security are embedded in the architecture from day one, not retrofitted later.
3. Overlooking Data Quality
Ingesting massive amounts of data from multiple sources is a lakehouse characteristic. But poor-quality data undermines all analytics efforts. Enterprises shouldn’t put data cleansing and validation on hold until after the platform is live, or else they’ll have to deal with inaccurate insights, reporting errors, and wasted resources.
Automated data quality checks, standardization processes, and real-time monitoring are non-negotiable. Investing in high-quality data right from the start ensures AI and analytics outputs are reliable. Besides fostering trust among decision-makers, strong data quality management reduces the risk of costly rework down the line.
4. Migrating Everything at Once
It’s often observed that enterprises try migrating all data and workloads to a lakehouse in one go. That’s where they err. Instead of accelerating adoption, as may be the belief, it leads to disruption and unforeseen technical challenges.
Phased migration is much more effective. Start with high-priority datasets and critical workloads, test integrations thoroughly, and iterate based on feedback. These initial deployments and performance optimization help teams learn and adapt to the new data environment. Gradually, confidence reflects across the organization. Incremental adoption even reduces risk and sets the stage for long-term success with the data lakehouse.
5. Neglecting Change Management and Skills Development
Lastly, user preparedness. Even the most well-architected lakehouse is bound to fail if the people using it are not prepared. Teams accustomed to traditional data warehouses or siloed data lakes may resist change, or may not leverage the lakehouse’s full potential. Organizations underestimating necessary cultural and operational changes are also part of the problem.
Change management is decisive. Enterprises should provide training for data engineers, analysts, and business users. Clearly communicating benefits and having team champions further fuels adoption goals. For optimum lakehouse outputs, upskilling employees in modern data practices, analytics tools, and SQL or Python for data manipulation is crucial as well.
Takeaway
Adopting a data lakehouse allows enterprises to streamline data management and enable more informed business decisions. But pitfalls abound if adoption is approached hastily or without careful planning.
From architectural missteps and governance oversights to underestimating data quality, migration challenges, and skills development, each mistake can have a domino effect across the organization. By acknowledging and addressing these common errors proactively, enterprises can reap the full benefits of a data lakehouse.