What is Data Lakehouse?
Data Lakehouse is A data architecture that combines the low-cost storage of data lakes with the structured querying and performance of data warehouses.
Definition
A data lakehouse merges two previously separate architectures: data lakes (cheap storage for raw, unstructured data) and data warehouses (structured, optimized storage for analytics queries). The lakehouse stores data in open formats (Parquet, Delta Lake, Iceberg) on object storage (S3, GCS, ADLS) while providing the ACID transactions, schema enforcement, and query performance traditionally associated with data warehouses. Databricks popularized the term and architecture, but Snowflake, BigQuery, and others have adopted similar concepts.
Why It Matters
Before lakehouses, companies maintained separate data lakes (for raw data and ML workloads) and data warehouses (for BI and analytics). This created data duplication, sync complexity, and higher costs. The lakehouse eliminates the need for two systems by providing warehouse-quality performance on lake-stored data. For B2B data operations, this means your enrichment data, intent signals, and CRM exports can live in one system that serves both analytics and machine learning workloads.
Example
A B2B company stores raw job posting data, website visitor logs, and CRM exports in a Delta Lake on S3. The data team runs SQL queries against this data using Databricks SQL for pipeline analytics and dashboards. The data science team trains intent prediction models on the same data using Spark. One storage layer serves both use cases without moving data between systems.
Best Practices for Data Lakehouse
Start with Clear Requirements
Before adopting any data lakehouse tooling, document what specific problems you need to solve. Teams that skip this step end up with tools that don't match their actual workflow. Write down your current pain points, the volume of data you handle, and the outcomes you expect.
Evaluate Against Your Existing Stack
The best data lakehouse solution is one that connects to what you already use. Check integration support with your CRM, data warehouse, and other tools before committing. A standalone tool that doesn't sync with your existing systems creates more work than it saves.
Measure Before and After
Set baseline metrics before you implement any changes to your data lakehouse process. Track data quality, time spent on manual tasks, and downstream conversion rates. Without a baseline, you can't prove ROI or identify regressions.
Build Internal Documentation
Document how data lakehouse fits into your data operations. Include which fields are affected, which systems are involved, and who owns the process. When team members leave or tools change, this documentation prevents knowledge loss.
Common Mistakes with Data Lakehouse
Treating It as a One-Time Project
Data Lakehouse requires ongoing attention. Data decays, requirements shift, and tools update their capabilities. Teams that set up a data lakehouse process and never revisit it end up with stale or broken workflows within 6 to 12 months.
Ignoring Data Quality Upstream
No amount of data lakehouse tooling fixes bad data at the source. If your input data is full of duplicates, formatting errors, or outdated records, the output will carry those same problems forward. Clean your source data first.
Over-Investing in Tools Before Process
Buying an expensive platform before you have a defined process for data lakehouse wastes money. Start with a clear workflow, test it manually or with basic tools, and then invest in automation once you know exactly what you need.
Not Auditing Results Regularly
Automated data lakehouse processes can drift over time. Schedule quarterly audits to check accuracy rates, coverage gaps, and whether the output still matches your team's needs. Catching issues early prevents compounding errors.
How Data Lakehouse Connects to Your Stack
Data Lakehouse rarely operates in isolation. It sits within a broader data and sales technology stack, and understanding where it fits helps you choose the right tools and build effective workflows.
CRM Systems
Your CRM is the central repository where data lakehouse data gets stored and used. Whether you run Salesforce, HubSpot, or another platform, the data lakehouse tools you choose should write data directly into CRM records without manual import steps.
Data Warehouses
For teams with analytics infrastructure, data lakehouse data often needs to flow into a data warehouse like Snowflake or BigQuery. This lets analysts build reports that combine data lakehouse signals with revenue data, usage metrics, and other business intelligence.
Sales Engagement Platforms
Outreach tools like Salesloft and Outreach rely on accurate data to personalize sequences. Data Lakehouse feeds these platforms with the information sales reps need to write relevant messages and target the right prospects at the right time.
Marketing Automation
Marketing platforms use data lakehouse data for segmentation, lead scoring, and campaign targeting. The more complete and accurate your data, the better your marketing automation performs across email, ads, and content personalization.