Data Lakehouse is a new and emerging data management architecture that combines the best parts of a data lake and a data warehouse.
Using a data lakehouse, you get the ability to store different types of data in a single platform and perform ACID-compliant queries and analytics.
So, why use a data lakehouse? Being a senior software engineer, I can understand how difficult it gets when you have to manage and maintain two separate systems and have large volumes of data flow from one to the other.
If you want to use your data for running business analytics and generating reports, you need to store structured data in a data warehouse. On the other hand, to store all the data coming from various data sources and in its original format, you need a data lake. Having a single lakehouse eliminates this need to maintain different systems as it brings the best of both worlds.
Significance of Data Lakehouse
In order to grow your organization and business, you need to be able to store and analyze data regardless of the format or structure. Data lakehouses are significant for modern data management because they address the limitations of both data lakes and data warehouses.
Your data lakes can often turn into data swamps, where data is dumped without any structure or governance. This makes it difficult to find and use the data, and it can also lead to data quality issues. On the other hand, having a data warehouse often leads you to be too rigid. It also becomes expensive.
A data lakehouse has its own set of characteristics. Let’s take a look at them.
Characteristics of a Data Lakehouse
Before you dive into the data lakehouse architecture, let’s see the most important features or characteristics of a data lakehouse.
It supports transactions – When you’re running a data lakehouse at a moderately large scale, there will be multiple reads and writes happening at the same time. Having ACID compliance ensures that concurrent reads and writes don’t hamper the data.
Support for Business Intelligence – You can add your BI tools directly to the indexed data. The need to copy the data somewhere else is eliminated. Additionally, you get the latest data in a reduced time and at a lower cost.
The Data Storage and Compute Layer are separated – With the two layers being separated, you can scale one of them without affecting the other. If you need more storage, you can add that without scaling up compute as well.
Support for Different Data Types – Because a data lakehouse is built on top of a data lake, it supports various types and formats of data. You can store and analyze various data types like audio, video, images, and text.
Openness in Storage Formats – Data lakehouses use open and standardized storage formats, like Apache Parquet. This allows you to plug in different tools and libraries in order to access the data.
Diverse Workloads are Supported – Using the data stored in a data lakehouse, you can perform a wide range of workloads. This includes queries through SQL, as well as BI, analytics, and machine learning.
Support for Real-time Streaming – You don’t need to create a separate data store and run a separate pipeline for real-time analytics.
Schema Governance – Data lakehouses promote robust data governance and auditing.
Data Lakehouse Architecture
Now, it’s time to take a look at the architecture of a data lakehouse. Understanding the data lakehouse architecture is key to understanding how it works. The data lakehouse architecture primarily has five major components. Let’s look at them one by one.
Data Ingestion Layer
This is the layer where all the different data in its various formats are captured. These could be data changes in your primary database, data from various IoT sensors, or real-time user data flowing through data streams.
Data Storage Layer
Once the data has been ingested from the various sources, it’s time to store them in their proper formats. This is where your storage layer comes in. Data can be stored in various mediums like AWS S3. Effectively, this is your data lake.
Metadata and Caching Layer
Now that you have your data storage layer in place, you need a metadata and data management layer. This provides a unified view of all the data present in the data lake. This is also the layer that adds ACID transactions to the existing data lake in order to transform it into a data lakehouse.
You can access the indexed data from the metadata layer using the API layer. These can be in the form of database drivers that let you run your queries through code. Or, these could be exposed in the form of endpoints that can be accessed from any client.
Data Consumption Layer
This layer comprises your analytics and Business Intelligence tools, which are the main users of the data from the data lakehouse. You can run your machine learning programs here to gain valuable insights from the data you have stored and indexed.
So, you now have a clear picture of the lakehouse architecture. But how do you build one?
Steps for Building a Data Lakehouse
Let’s look at how you can build your own data lakehouse. Whether you have an existing data lake or warehouse or you’re building a lakehouse from scratch, the steps remain similar.
Identify the Requirements – This includes identifying what types of data you’ll be storing and what use cases you want to target. These may be your machine learning models, business reporting, or analytics.
Create an Ingestion Pipeline – The data ingestion pipeline is responsible for bringing the data into your system. Based on the source systems that are generating the data, you might want to go for messaging buses like Apache Kafka or have API endpoints exposed.
Build the Storage Layer – If you already have a data lake, then that can act as the storage layer. Otherwise, you can choose from various options like AWS S3, HDFS, or Delta Lake.
Apply Data Processing – This is where you extract and transform the data based on your business requirements. You can use open-source tools like Apache Spark to run pre-determined periodic jobs that will ingest and process the data from your storage layer.
Create Metadata Management – You need to track and store the various kinds of data and their corresponding properties so that they can be easily cataloged and searched when required. You might also want to create a caching layer.
Provide Integration Options – Now that your primary lakehouse is ready, you’ll need to provide integration hooks where external tools can connect and access the data. These could be SQL queries, machine learning tools, or Business Intelligence solutions.
Implement Data Governance – Because you’ll be working with various kinds of data from different sources, you need to establish data governance policies, including access control, encryption, and auditing. This is to ensure data quality, consistency, and compliance with regulations.
Next, let’s look at how you can migrate to a data lakehouse if you have an existing data management solution.
Steps for Migrating to a Data Lakehouse
When you’re migrating your data workload to a data lakehouse solution, there are certain steps that you should keep in mind. Having a plan of action lets you avoid last-minute issues.
Step 1: Analyze the Data
The initial and one of the most crucial steps for any successful migration is data analysis. With proper analysis, you can define the scope of your migration. Furthermore, it lets you identify all additional dependencies that you may have. Now, you have a greater overview of your environment and what you’re about to migrate. This enables you to prioritize your tasks better.
Step 2: Prepare the Data for Migrations
The next step for a successful migration is data preparation. This includes the data you’ll be migrating, as well as the supporting data frameworks you’ll be needing. Rather than blindly waiting for all your data to be available in your lakehouse, knowing which datasets and columns you actually need can save valuable time and resources.
Step 3: Convert the Data to the Required Format
You can leverage auto conversion. In fact, you should prefer auto-conversion tools as much as possible. Data conversions when migrating to data lakehouse can be tricky. Luckily, most tools come with easily readable SQL code or low-code solutions. Tools like Alchemist help with this.
Step 4: Validate the Data after Migration
Once your migration is complete, it’s time to validate the data. Here, you should try to automate the validation process as much as possible. Otherwise, manual migration becomes tedious and slows you down. It should be used only as a last resort. It’s important to verify that your business processes and data jobs remain unaffected post-migration.
Key Features of Data Lakehouse
🔷 Complete Data Management – You get data management features that help you make the most out of your data. These include data cleansing, ETL or Extract-Transform-Load process, and schema enforcement. Thus, you can readily sanitize and prepare your data for further analytics and BI (Business Intelligence) tools.
🔷 Open Storage Formats – The storage format in which your data is saved is open and standardized. This means that the data you are collecting from different data sources are all stored similarly, and you can work with them right from the beginning. It supports formats such as AVRO, ORC, or Parquet. Additionally, they support tabular data formats as well.
🔷 Separation of Storage – You can decouple your storage from the compute resources. This is achieved by using separate clusters for both. Hence, you can separately scale up your storage as necessary without having to unnecessarily make any changes to your compute resources.
🔷 Data Streaming Support – Making data-driven decisions often involves consuming real-time data streams. Compared to a standard data warehouse, a data lakehouse gives you the support of real-time data ingestion.
🔷 Data Governance – It supports strong governance. Additionally, you also get auditing capabilities. These are especially important to maintain data integrity.
🔷 Reduced Data Costs – The operational cost of running a data lakehouse is comparatively less than a data warehouse. You can get cloud object storage for your growing data needs for less price. Additionally, you get a hybrid architecture. Thus, you can eliminate the need to maintain multiple data storage systems.
Data Lake vs. Data Warehouse vs. Data Lakehouse
Stores raw or unstructured data
Stores processed and structured data
Stores both raw as well as structured data
Doesn’t have a fixed schema
Has a fixed schema
Uses open-source schema for integrations
Data is not transformed
Extensive ETL is required
ETL is done as needed
No ACID compliance
Typically slower as data is unstructured
Very fast because of structured data
Fast because of semi-structured data
Storage is cost-effective
Higher storage and query costs
Storage and query cost is balanced
Requires careful governance
Strong governance needed
Supports governance measures
Limited real-time analytics
Limited real-time analytics
Supports real-time analytics
Data storage, exploration, ML and AI
Reporting and analysis using BI
Both machine learning and analytics
By seamlessly combining the strengths of both data lakes and data warehouses, a data lakehouse addresses important challenges that you might face in managing and analyzing your data.
You now know about the characteristics and architecture of a lakehouse. The significance of a data lakehouse is evident in its ability to work with both structured and unstructured data, offering a unified platform for storage, query, and analytics. Additionally, you also get ACID compliance.
With the steps mentioned in this article about building and migrating to a data lakehouse, you can unlock the benefits of a unified and cost-effective data management platform. Stay on top of the modern data management landscape and drive data-driven decision-making, analytics, and business growth.
In the information age, data centers collect large amounts of data. The data collected comes from various sources such as financial transactions, customer interactions, social media, and many other sources, and more importantly, accumulates faster.
DataFrames are a foundational data structure in R, offering the structure, versatility, and tools necessary for data analysis and manipulation. Their importance extends to various fields, including statistics, data science, and data-driven decision-making across industries.
Power Your Business
Some of the tools and services to help your business grow.
The text-to-speech tool that uses AI to generate realistic human-like voices.