What is Data Lakehouse?
A Data Lakehouse is a data platform that enables organizations to store, manage, and analyze large volumes of data in real-time. It is built on top of Azure Databricks, which combines big data and data warehousing to provide a unified experience for data ingestion, preparation, management, and analysis.
A Data Lakehouse is a data platform that enables organizations to store, manage, and analyze large volumes of data in real-time. It is built on top of Azure Databricks, which combines big data and data warehousing to provide a unified experience for data ingestion, preparation, management, and analysis.
The two-tier data architectures commonly used in enterprises today are highly complex for both the users and the data engineers building them, regardless of whether they are hosted on-premises or in the cloud.
Lakehouse architecture reduces the complexity, cost, and operational overhead by providing many of the reliability and performance benefits of the data warehouse tier directly on top of the data lake, ultimately eliminating the need for a separate warehouse tier.
Benefits of a Data Lakehouse
Data Lakehouse architecture combines the data structure and management features with the low-cost storage and flexibility of a data lake. The benefits of this implementation are enormous and include:
A) Reduced Data Redundancy: Data Lakehouse reduces data duplication by providing a single, all-purpose data storage platform to cater to all business data demands. Because of the advantages of both data warehouses and data lakes, most companies opt for a hybrid solution. However, this approach could lead to data duplication, which can be costly.
B) Cost-Effectiveness: Data Lakehouse implements the cost-effective storage features of data lakes by utilizing low-cost object storage options. Additionally, Data Lakehouse eliminates the cost and time associated with maintaining multiple data storage systems by providing a single solution.
C) Support for a Wider Variety of Workloads: Data Lakehouse provides direct access to some of the most widely used business intelligence tools (such as Tableau and PowerBI) to enable advanced analytics. Additionally, Data Lakehouse uses open-data formats (such as Parquet) with APIs and machine learning libraries, including Python and R, making it straightforward for data scientists and machine learning engineers to utilize the data.
D) Ease of Data Versioning, Governance, and Security: Data Lakehouse architecture enforces schema and data integrity, making it easier to implement robust data security and governance mechanisms
E) Improved Data Integration: Instead of using SSIS to move data around, Data Lakehouse uses Azure Data Factory, which serves as a modern replacement for SSIS as your integration engine.
Data Warehousing Evolution
The transition to Lakehouse reflects the evolution of data management needs and technology capabilities. Here is a brief explanation of each:
Data Warehouse
A data warehouse is a centralized repository that stores structured data from multiple sources, typically for business intelligence and reporting purposes. The data is extracted, transformed, and loaded (ETL) into a structured format optimized for analysis. Traditionally, data warehouses were limited in capacity by the processing power of a single structured query language (SQL) Server.
Data warehouses did not initially include the concept of OLAP (Online Analytical Processing), which emerged in the 1990s and early 2000s. OLAP is a classification of software technology that enables analysts, managers, and executives to gain insights into information through fast, consistent, interactive access to a various views of data transformed from raw information to reflect the real dimensionality of the enterprise.
Data Lake
A data lake is a large, cloud-based centralized repository designed to store vast amounts of structured, semi-structured, and unstructured data in its raw form. The data is stored in its native format and can be accessed and processed by a variety of tools and platforms, such as Hadoop and Spark, making data lakes ideal for big data processing and machine learning.
However, the primary challenge with data lakes is that they lack structure and are not easily queried. As a result, organizations often end up storing unorganized low-quality data, which can hinder their ability to extract valuable insights.
Organizations initially adopted data lakes to train machine learning models and handle large-scale data processing tasks. While effective for these purposes, the absence of structure and data management features often led to a chaotic accumulation of data, sometimes referred to as "data swamps."
Lakehouse
An innovative approach that combines the benefits of data warehousing and data lakes, Data Lakehouse enables organizations to store data in its raw form, like a data lake, while also providing the structure and performance optimizations of a data warehouse. Lakehouse typically uses tools such as Apache Spark and Delta Lake to provide a unified platform for data processing, analytics, and machine learning.
The shift from data warehouses to data lakes was driven by the need for more flexible and scalable data storage solutions. Data lakes allowed organizations to store and process large volumes of diverse data types without the need for expensive ETL processes. However, data lakes lacked the structure and performance optimizations of data warehouses, which limited their usefulness for complex analytics and reporting.
The emergence of Lakehouse addresses these limitations by providing a unified platform that combines the flexibility and capacity of data lakes with the structure and performance optimizations of data warehouses. Lakehouse also enables organizations to leverage greater flexibility and scalability at a lower cost.
Databricks is built on Apache Spark, which enables a massively scalable engine that runs on compute resources decoupled from storage.
The Databricks Lakehouse uses two additional key technologies:
- Delta Lake: An optimized storage layer that supports ACID transactions and schema enforcement.
- Unity Catalog: A unified, fine-grained governance solution for data and AI.
Some Limitations of Data Lakehouse
Data Lakehouse architecture, which combines the capabilities of data lakes and data warehouses, offer significant advantages in terms of flexibility, scalability, and analytics capabilities. However, they also come with their own set of limitations and challenges:
Diverse Skillset Requirements
To implement and use Data Lakehouse, you need to have a team with diverse skill sets:
- Python Expertise: Given all Databricks notebooks are written in Python, experience with this language is essential. Organizations need team members proficient in Python to effectively work with Databricks.
- Data Integration Skills: Instead of relying on SSIS (SQL Server Integration Services) to move data around, Data Lakehouse uses Azure Data Factory. Team members should be skilled in SSIS and Azure Data Factory to handle data integration tasks.
- SQL and Report Development Skills: Proficiency in SQL and report development is necessary maintain and utilize Data Lakehouse efficiently.
Complexity in Building the Lakehouse
The process for building a Lakehouse involves multiple steps:
- Pipeline Construction: For each data entity you want to bring in, you need to construct an Azure Data Factory pipeline. This pipeline moves the data into a data lake in what is called the ‘bronze layer’, where data is essentially raw and uncleaned from the source system.
- Data Transformation: Databricks notebooks provide instructions for transforming raw data from the bronze layer to curated data in the ‘silver layer’, meaning the data has been standardized brought into a common format across several disparate systems.
- Data Presentation: Ultimately, the data is moved to the ‘gold layer, which is precompiled for presentation and to be consumed by power BI
This process can become time-consuming, taking weeks or more, for each data entity.
Overcoming Data Lakehouse Limitations with the Accelerator
To overcome the limitations of Data Lakehouse architecture, our organization developed the Accelerator.
What is the Accelerator?
The Accelerator was created by developing generic Azure Data Factories and generic Azure Databricks notebooks. These tools read instructions from a database, which contains a list of steps that each tool must perform on the data to move it from the source into the bronze layer, then to the silver layer, and finally into the gold layer. This structured process ensures that data is presented in the desired format.
Benefits of the Accelerator:
Reduced Implementation Time: The Accelerator significantly reduces the time needed to implement the latest Microsoft Lakehouse best practices.
Simplified Configuration: The configuration simplifies environment components while increasing usability for the client.
No Need for Specific Knowledge: Organizations using the Accelerator do not need specific knowledge about Azure Data Factories, Azure Databricks, or Python.
Streamlined Data Processing: The structured process ensures data is efficiently moved and transformed through the bronze, silver, and gold layers, ensuring it is presented in the desired format.
Our Accelerator Integration Service
We offer the Accelerator as an integration service. By paying a nominal monthly fee, we handle all your data integration from your source system into your Data Lakehouse. There is no large upfront license fee.
Key Features of Our Service:
- Managed Azure Appliances: We manage all the Azure appliances necessary to run your integrations.
- Data Ownership: You retain ownership of all your data. The data will reside in a Data Lakehouse on your tenant in Azure, but the appliances for running the integration will be hosted on our hardware in our Azure cloud.
- Low Effort, Low Maintenance: This service requires minimal effort and maintenance from your side, allowing you to focus on leveraging your data without worrying about the underlying infrastructure.
