Building a Data Lakehouse: The Definitive 2024 Guide

Intro

You want to store unstructured data in a data lake, but you also want to store structured data in a data warehouse. Previously, you would have needed two discrete systems. Today however, you just need to build a data lakehouse. 

A data lakehouse combines the best of both worlds, offering several key benefits and positioning your organization for effective data use – and flowing from that, long-term success.

Read on to discover ​​how to build a data lakehouse, including the necessary steps, tools, and best practices. 

What Is A Data Lakehouse?

A data lakehouse combines the features of both data lakes and data warehouses, aiming to merge the best aspects of both systems. 

1. Data lake: A data lake stores vast amounts of raw, unstructured data from various sources in its native format. While it offers scalability and flexibility, it lacks some of the data management and querying capabilities of a traditional data warehouse.
2. Data warehouse: A data warehouse, on the other hand, stores structured data and is optimized for complex querying and analytics but can be more rigid and expensive, especially for handling large volumes of raw or semi-structured data.

Features of a Data Lakehouse

• Unified storage: Like a data lake, a lakehouse can store both structured and unstructured data (e.g., text, video, logs, and images).
• ACID transactions: It offers support for ACID (Atomicity, Consistency, Isolation, Durability) transactions, a feature typically associated with data warehouses, which ensures data reliability and integrity.
• Efficient querying: It combines the capability of SQL-style queries from data warehouses with the flexibility of data lakes, making it easier for analysts and data scientists to access, process, and analyze large datasets.
• Cost efficiency: Since a lakehouse can store data in its raw form like a data lake, it eliminates the need for duplicating data between a data lake and a warehouse, thus reducing storage costs.
• Machine learning and advanced analytics: By housing both structured and unstructured data, a lakehouse can support advanced analytics and machine learning workflows without needing multiple systems.

So a data lakehouse offers the flexibility of data lakes with the management, governance, and performance of data warehouses, catering to modern data needs where large volumes of data must be efficiently stored, queried, and analyzed.

The Benefits of a Data Lakehouse

Building a data lake house has several benefits that can be a significant boost to your business. These include:

• Simplified architecture: Instead of having two separate systems (a data lake and a data warehouse), the lakehouse consolidates data into one platform. For example, an e-commerce company can store both clickstream and transactional data in a lakehouse, enabling analysts to access all data from one source for seamless insights.
• Cost efficiency: Related to this, having one data lakehouse reduces storage duplication by storing raw and processed data in one system. If a financial services firm wants to store raw transactional data and processed reports, they can build a data lakehouse and avoid additional storage costs.
• Scalability: Like data lakes, lakehouses can scale to handle vast amounts of data while retaining the analytical capabilities of data warehouses. Social media platforms might use a lakehouse to store user-generated content and scale as the number of posts and videos grows.
• Real-time analytics: With its ability to support both streaming and batch processing, a lakehouse can facilitate real-time analytics alongside historical data analysis. An example here is a healthcare provider that can build a data lakehouse to train models on patient records and medical images to predict treatment outcomes.

Comparing Data Lakehouses vs. Data Lakes vs. Data Warehouses

When considering building a data lakehouse, it’s important to understand the similarities and differences between – and typical use cases of – data lakehouses, data lakes, and data warehouses:

1. Data Lakehouse

• Purpose: Combines the flexibility of data lakes with the data management and performance capabilities of warehouses.
• Data type: Stores both structured, semi-structured, and unstructured data.
• Analytics: Supports both advanced analytics and traditional BI with better querying performance.
• Use case: Suitable for organizations needing a unified platform for real-time analytics, machine learning, and BI.

2. Data Lake

• Purpose: Designed to store vast amounts of raw, unstructured data from various sources.
• Data type: Primarily unstructured and semi-structured data (e.g., logs, images, videos).
• Analytics: Requires additional tools to process and analyze data, often making querying complex and slow.
• Use case: Ideal for data storage and processing when there’s a need for raw data preservation and flexibility in data formats.

3. Data Warehouse

• Purpose: Optimized for structured data storage and complex querying.
• Data type: Structured data only, typically organized in tables.
• Analytics: Best suited for traditional BI and reporting; fast querying but lacks support for unstructured data.
• Use case: Ideal for reporting and analytics on highly structured, cleaned data.

Key Differences:

• Flexibility: Data lakes are more flexible but require more work for analysis; warehouses are fast for structured queries, but less flexible. Lakehouses blend both, allowing efficient analysis of both structured and unstructured data.
• Cost: Data lakes are cheaper to scale but incur additional processing costs; warehouses are more expensive for large datasets, while lakehouses aim to balance cost efficiency by avoiding redundant storage.

How To Build A Data Lakehouse

Building a data lakehouse involves integrating elements of both data lakes and data warehouses to create a unified data platform. Here’s a high-level step-by-step guide:

1. Define Business Requirements

• Goal: Identify what data types (structured, semi-structured, unstructured) you will handle and the analytics use cases (real-time analytics, machine learning, BI).
• Action: Collaborate with stakeholders from various departments (e.g., finance, operations, data science) to understand their data needs.

2. Choose a Data Lakehouse Platform

• Action: Select a platform that supports lakehouse architecture such as SQream or Panoply. 

3. Set Up Storage Layer (Data Lake)

• Action 1: Implement cloud storage (e.g., AWS S3, Google Cloud Storage, or Azure Data Lake Storage) for raw, semi-structured, and unstructured data.
• Action 2: Organize data in directories or partitions based on business needs (e.g., date, region, product).

4. Set Up Metadata Layer

• Action: Create a metadata management system to track the schema and structure of your data.

5. Integrate Data Processing Engine

• Action: Use a data processing engine to process both batch and real-time data.

6. Implement ACID Transactions

• Action 1: Ensure ACID compliance. This enables consistent, reliable updates and deletes across the lakehouse.
• Action 2: Configure version control to allow easy data rollback and ensure data integrity.

7. Set Up Data Governance and Security

• Action: Implement security protocols, such as role-based access control (RBAC) and encryption for sensitive data.

8. Data Indexing and Query Optimization

• Action 1: Optimize query performance by indexing or partitioning your data 
• Action2: Configure caching mechanisms to speed up frequently accessed data.

9. Set Up BI Tools for Analytics

• Action 1: Connect your lakehouse to analytics and BI tools like Tableau, Power BI, or Looker.
• Action 2: Set up dashboards and reports to monitor KPIs and enable data-driven decision-making.

10. Deploy and Maintain

• Action: Launch the lakehouse and ensure continuous monitoring 

By following these steps, you can build a scalable, unified data lakehouse that handles raw data, enables analytics, and supports machine learning and BI needs.

Best Practices for Building a Data Lakehouse

Here are some best practices for building a data lakehouse:

1. Start with clear business requirements
   • Align the lakehouse architecture with specific business goals, like enabling real-time analytics or improving machine learning workflows. Work with cross-functional teams to understand data needs from multiple perspectives (data engineers, analysts, etc.).
2. Partition and index data for better query performance
   • Partition data based on common query parameters (e.g., date, region) to improve query speed. Use indexing and data compression techniques to reduce query time and storage costs.
3. Support multiple workloads (batch and streaming)
   • Ensure the lakehouse supports both batch processing and real-time analytics, enabling diverse use cases.
4. Monitor and optimize performance
   • Continuously monitor the performance of queries, data pipelines, and storage to avoid bottlenecks. Set up alerting systems to detect performance issues.
5. Encourage data collaboration
   • Ensure that the lakehouse facilitates collaboration between data engineers, analysts, and data scientists. If possible, set up a centralized data catalog and metadata management tool to ensure data discoverability.

Some common pitfalls to avoid when building a data lakehouse include:

1. Overcomplicating the architecture: Using too many disparate tools and services can create complexity and technical debt. Rather than stick to an integrated platform wherever possible to simplify architecture.
2. Neglecting data quality management: Poor data quality leads to unreliable analytics and decision-making. Implement data validation and cleansing processes to ensure data accuracy.
3. Failing to optimize for query performance: Slow queries can impact productivity and make real-time analytics impossible. Use proper indexing, partitioning, and caching strategies for optimized query performance.
4. Not accounting for scalability: A lakehouse that doesn’t scale effectively can lead to performance issues as data grows. Choose a solution that can scale dynamically with increasing data volumes.
5. Neglecting near real-time processing capabilities: Failing to enable near real-time processing can limit the use of streaming data. Make sure that the architecture supports both batch and stream processing.

By following these best practices and avoiding common pitfalls, organizations can build an efficient, scalable, and secure data lakehouse that meets their business goals.

FAQ

Q: How does a data lakehouse improve data management?

A: Building a data lakehouse improves data management by combining the flexibility of a data lake with the governance and performance of a data warehouse. It enables organizations to store and manage all types of data (structured, semi-structured, and unstructured) in one system, while also supporting ACID transactions and providing better data governance, quality control, and querying performance. This helps streamline analytics, reduce data duplication, and improve overall efficiency.

Q: What are the costs involved in building a data lakehouse?

A: The costs of building a data lakehouse vary depending on factors like infrastructure (cloud vs. on-premises), tools and platforms used (e.g., Databricks, AWS), data storage needs, and personnel expertise. Costs typically include cloud storage fees, data processing costs, platform subscriptions, and maintenance. Depending on the scale and complexity, it can range from a few thousand dollars a month for small implementations to millions annually for large enterprises.

Q: How long does it take to build a data lakehouse?

A: The time to build a data lakehouse depends on the complexity of the architecture and data requirements. For small-to-midsize companies, it may take a few months (3-6 months) to set up the initial infrastructure, ingest data, and build analytics pipelines. For large-scale enterprises with extensive data needs and custom integrations, it could take 6-12 months or more to fully implement and optimize the system.

Meet SQream: Industry-leading GPU-accelerated data processing

If you’re looking for the ultimate data lakehouse solution, look no further than SQream Blue. SQream Blue is a SQL data lakehouse that empowers organizations to transform and query datasets to gain deeper, time-sensitive insights: at one third the cost and three times the speed of cloud warehouse and query engine solutions.

SQream Blue is a cloud-native, fully managed data lakehouse platform designed to address the bottlenecks that digital-first businesses encounter when preparing large datasets for analytics and AI/ML tasks. The platform leverages a patented GPU-acceleration engine, allowing for fast, reliable, and cost-effective data processing. Its architecture ensures that raw data can be transformed into analytics-ready formats for BI or ML, all while keeping the data securely in the customer’s own cloud storage.

Key advantages of SQream Blue are its scalability and speed. It offers petabyte-scale data pipelines and enables data preparation tasks such as denormalization, pre-aggregation, and feature generation at GPU speeds, making it up to twice as fast as other solutions. The platform integrates easily with industry-standard tools and open-source workflow management systems, and its optimized processing engine takes advantage of Apache Parquet’s structure to improve performance. 

SQream aims to empower businesses by unlocking the value of their massive datasets, providing superior performance at a lower cost.

Summary: Building a Data Warehouse in 2024

In this Guide, we’ve explored the concept of building a data lakehouse, including practical steps, how-to’s, best practices, and more. 

The benefits of building a data lakehouse are significant – so much so that implementing a data lakehouse can be a key differentiator for your organization, as you store and leverage data more effectively than competitors.

For questions around building a data lakehouse, and to implement your own, get in touch with the SQream team.