Here we cover what a Data Lakehouse is and its primary features, the history of data architecture, the differences between different types of data management architecture and the challenges and benefits that come with a Data Lakehouse.

A new data management architecture has recently emerged known as the Data Lakehouse, which combines the best of data lakes and data warehouses. If you are interested in learning more about Data Lakehouse and how you can use it to support your business needs from data, this guide to Data Lakehouse will show you how you can leverage this system in your organization. Here we cover what a Data Lakehouse is and its primary features, the history of data architecture, the differences between different types of data management architecture and the challenges and benefits that come with a Data Lakehouse.

To understand what a Data Lakehouse is, you first need to know what a data lake and a data warehouse are.

What Is a Data Lake?

After companies started collecting significant amounts of data from various sources, data architects envisioned a system that could house data for several analytic workloads and products close to the source system format, which led to the development of data lakes.

A data lake is a repository for raw data in different formats. Data Lakes can provide comprehensive data storage which could potentially power machine learning (ML), business intelligence (BI) and data analytics needs. Though a Data Lake helped solve data storage needs for extremely high volumes of heterogeneous data, however the architecture lacked emphasis to certain features, such as the ability to enforce data quality, support change data capture (CDC) and maintain consistency.

This lack of emphasis to maintain consistency in data made it impossible to be considered as a trusted source for performing enriched analytics. Due to this very nature a Data Lake was considered as complementary to a data warehouse, not a replacement.

What Is a Data Warehouse?

On the contrary a Data Warehouse has a long history in BI applications and decision support. Since its inception, Data Warehouse methodologies have continued to evolve. Then came Massively parallel processing (MPP) architectures which have led to systems that can handle greater data volumes.

Though data warehouses were useful for analyzing structured data, modern enterprises also needed to deal with semi-structured and unstructured data. Both of these bring in additional complexities due to are high volume, velocity and variety. Data can be structured, unstructured, semi-structured or textual.

• Structured data: Typically, structured data is transaction-based data that a business generates to conduct everyday activities.
• Unstructured data: Unstructured data has different sources like video, image, analog-based and data from the Internet of Things (IoT).
• Semi-structured data: Semi-structured data has some structure but doesn't conform to a data model.
• Textual data: Textual data is generated by email, letters and conversations that occur within the corporation.

Data warehouses are not optimized for unstructured data like images, audio, video and text. A data warehouse is not suitable for many of these data uses, nor is it the most cost-effective option. Many organizations needed an architecture that could cater to diverse data needs, such as ML, data science, SQL analytics and real-time monitoring.

At RightData, Dextrus is our data warehousing solution. Use Dextrus to simplify data management and migration with data warehousing tools. This unified data platform comes with the required data engineering capacity to create scalable data warehouses and ensure stability, safety and efficiency when you move information.

The Data Lakehouse Solution

Since data lakes and data warehouses both have their limitations, there was still a need for a high-performance, flexible system. This need led to many businesses using multiple systems, including a data lake and multiple data warehouses. These complex systems can quickly become overwhelming and lead to delays. This is where a data lakehouse comes in. A data lakehouse combines the best features of data warehouses and data lakes to overcome the limitations of both systems.

Chapter 1: What Is a Data Lakehouse?

Think of your favorite combinations — peanut butter and jelly, chocolate and peanut butter, coconut and lime. A data lakehouse is similar. This data management architecture is a combination of data lakes and data warehouses, bringing together data management systems and cloud storage.

Data Lakehouses: A Modern Data Management Solution

To address the limitations of a data lake, data lakehouses emerged as a new modern data management solution. The best elements of data warehouses and data lakes were combined to create this new, open architecture. Data lakehouses combine the scale, flexibility and cost-efficiency of a data lake with a data warehouse's transactions and data management. This enables machine learning (ML) and business intelligence (BI) on all data. A data lakehouse also eliminates the worst concepts of each model, which can add more benefits to your business.

How Data Lakehouses Combine Data Lakes and Warehouses

The new design of a data lakehouse implements similar data management features and data structures as those in data warehouses. These are directly implemented on top of cost-effective cloud storage in an open format. Essentially, data lakehouses are a redesign of data warehouses in today's world now that highly reliable and affordable storage is available. Object stores are commonly used in data lakehouses, providing highly available, low-cost storage.

Merging data warehouses and data lakes into one system means your data team can move more quickly, using data without needing to pull from several different systems. A data lakehouse user can access multiple standard tools for non-BI workloads, such as ML and data science. Data refinement and exploration are both standard for many data science and analytic applications.

Why Implement a Data Lakehouse in Your Business

With this new data management architecture, your business can radically simplify enterprise data infrastructure. Since ML can disrupt any industry today, you can also use a data lakehouse to accelerate innovation. Previously, most of a company's data that went into decision-making or products was structured data gathered from operational systems. Today, many products incorporate artificial intelligence (AI) via text mining, speech models and computer vision models.

Compared to a data lake, using a data lakehouse for AI provides you with data versioning, security, and governance required for unstructured data. Depending on your business needs, you may favor some tools over others, such as BI tools and integrated development environments (IDEs). To integrate all these tools, a data lakehouse needs a good user interface and the ability to address issues that may arise as technology develops. Fortunately, over time, a data lakehouse can retain its simplicity and cost-efficiency while serving diverse data applications.

Who Should Use a Data Lakehouse

Organizations that want to graduate from BI to AI to progress in their analytics journey may want to consider incorporating data lakehouse architecture. Businesses are increasingly turning to unstructured data to advise their decision-making and data-driven operations due to the richness of insights that a data lakehouse can provide.

For instance, if you count how many customers enter your establishment every day and store this data, this number will only give you a single data point. However, if you also have video of customers entering your establishment, you can gather much more data, such as their demographics, clothing types and even their moods. Though you can dump all of this information in a data lake, you could face data governance issues because you're storing personal information. A data lakehouse addresses this via automated compliance procedures.

At RightData, we offer our Dextrus solution, a unified data platform with several capabilities related to wrangling, preparation, streaming and self-service data ingestion.

Primary Features of a Data Lakehouse

Due to its cloud storage, a data lakehouse is considered a hybrid approach. To understand what sets a data lakehouse apart and how you can use it in your business, you first need to know its primary features. The key features of a data lakehouse include the following:

1. Openness

One of the main features of a data lakehouse is openness. A data lakehouse's storage format is open and standardized. An open format is a file format usable in several software programs and has openly published specifications. Standardized file formats that a data lakehouse can store data may include Optimized Row Columnar (ORC) and Apache Parquet.

Data lakehouses also offer an application programming interface (API), allowing several engines and tools to access data efficiently and directly. Engines and tools may include Python libraries and ML.

2. Business Intelligence Support

A data lakehouse enables the direct use of BI tools on the source data. BI support improves recency, minimizes latency, reduces staleness and lowers the cost of using copies of the data in both a data warehouse and a data lake. This eliminates the need to have a copy in a data warehouse in a fitting form.

3. ACID Transaction Support

Atomicity, consistency, isolation and durability (ACID) are all defining properties of a transaction. They also ensure data reliability and consistency. Data pipelines frequently read and write data concurrently in an enterprise lakehouse. Support for ACID transactions ensures consistency as several parties simultaneously read and write data, usually using SQL. ACID transactions may be available in data warehouses, but data lakehouses can apply them to a data lake as well, solving the issue of low-quality data.

4. End-to-End Streaming

In many enterprises, real-time reports are an essential component of operations. A data lakehouse offers support for end-to-end streaming that eliminates the need to have separate systems for real-time data applications. An example of real-time data that a lakehouse supports is a stream from devices connected to the Internet of Things (IoT).

5. Support for Diverse Workloads

Data lakehouses also offer support for diverse workloads, such as ML, data science, SQL and analytics. Though organizations may need several tools to support each of these workloads, they all depend on the same data repository.

6. Support for Diverse Data Types

A data lakehouse can support various data types, including both structured and unstructured data. You can use a data lakehouse to refine, store, access and analyze data types needed for several data applications, including:

• Text
• Audio
• Video
• Images
• Semi-structured data

Unlike a data warehouse that can deal with only structured data, a data lakehouse allows for a greater variety of data formats like PDF files, text documents and system logs.

7. Storage Decoupled From Compute

Storage decoupled from compute means computing and storing use separate clusters. This separation allows these systems to scale to larger data sizes and more concurrent users. With direct access to the same storage, various users and apps can run concurrent queries on a separate computing node. A few modern data warehouses may also have this property.

8. Schema Enforcement and Governance

The data lakehouse should be able to support schema governance and enforcement. This allows the data lakehouse to support data warehouse schema architectures like star- or snowflake-schemas. The system should have robust auditing and governance mechanisms and the ability to reason about data integrity. Data governance features include auditing and access control. You can also control the schema of your tables with the support of schema enforcement and evolution. Schema enforcement prevents the accidental upload of useless data, and evolution enables the automatic addition of new columns.

These key features of data lakehouses can benefit your business. While tools for access control and security are basic requirements, an enterprise grade system needs additional features. With recent privacy regulations, data governance capabilities have become essential, including lineage, retention and auditing. You may also need tools that enable data discovery like data usage metrics and data catalogs. With a data lakehouse, you only need to implement, administer and test these enterprise features for one system.