Evaluating Low-Code Lakehouse Platforms: Challenges & Practice

Solutions Review editors compiled this article to make evaluating low-code lakehouse platforms an easier task. We also recommend this exclusive low-code lakehouse platforms resource from the folks at Prophecy.io.

A low-code lakehouse platform is an emerging technology that combines the benefits of low-code development and data lakehouses. Low-code development is a software development approach that enables the rapid development of applications with minimal coding, using visual interfaces and drag-and-drop components. Data lakehouses, on the other hand, are data architectures that allow organizations to store large amounts of structured and unstructured data in a single location for analysis. Evaluating low-code lakehouse platforms can be a daunting task.

Low-code lakehouse platforms combine these two technologies to provide a development environment that allows developers to quickly build data-driven applications that can access and analyze data from the data lakehouse. The platform offers a visual interface that enables developers to create applications by assembling pre-built components rather than writing code from scratch. These components can include data connectors, analytics functions, and visualization tools.

Building a data lakehouse with Apache Spark and Delta Lake requires combining technical expertise and data management skills. However, following these steps, you can implement a robust and scalable data architecture to support your organization’s data-driven decision-making needs.

Low-Code Lakehouse Platforms

Top Data Lakehouse Challenges

While lakehouse platforms offer many benefits, such as scalability and flexibility, they also come with several challenges, beginning with data quality. In a data lakehouse, data is stored in its raw form, which means that data quality issues can be propagated throughout the system. To overcome this challenge, organizations must implement robust data quality controls, such as data profiling, data cleansing, and data validation.

Data governance is also an essential aspect of any data architecture, and data lakehouses are no exception. Organizations must ensure that the data lakehouse data is secure, compliant, and well-managed. This includes implementing data access controls, retention policies, and auditing and monitoring procedures.

One of the key benefits of a data lakehouse is the ability to integrate multiple data sources. However, integrating data from various sources can be a complex and time-consuming. Organizations must implement robust data integration tools and processes to ensure that data is integrated effectively and efficiently.

Data lakehouses can be complex, with many components and technologies working together. This can make it challenging to manage and maintain the system, particularly for organizations that lack the necessary expertise. Organizations must invest in training and support for their data lakehouse teams to ensure that they can manage the system effectively.

Data lakehouses can be slow to query and analyze, particularly when dealing with large volumes of data. To overcome this challenge, organizations must implement data indexing, partitioning, and compression techniques, as well as optimizing query performance.

Low-Code Lakehouse Platforms: Building a Data Lakehouse

Building a data lakehouse with Apache Spark and Delta Lake involves several steps, including:

Defining the Data Architecture

Before you build the data lakehouse, you need to define the data architecture, including the data sources, data storage, data processing, and data access layers. This will help you determine the tools and technologies you need to implement.

Installing Apache Spark

Apache Spark is a distributed computing platform that provides powerful data processing capabilities. You need to install and configure Apache Spark on your system before you can start building the data lakehouse.

Installing Delta Lake

Delta Lake is an open-source storage layer that provides ACID transactions, versioning, and schema enforcement capabilities to Apache Spark. You must install and configure Delta Lake on top of Apache Spark to implement a robust and scalable data lakehouse.

Ingesting Data

Once you have Apache Spark and Delta Lake installed, you can start ingesting data from various sources into the data lakehouse. This can include structured data from databases, semi-structured data from APIs, and unstructured data from log files.

Cleaning & Transforming Data

After ingesting the data, you need to clean and transform it to ensure that it is accurate and consistent. This can include removing duplicates, standardizing formats, and correcting errors.

Storing Data

Once the data is cleaned and transformed, you must store it in the Delta Lake storage layer. Delta Lake provides partitioning, clustering, and indexing to enable efficient data storage and retrieval.

Query & Analyze Data

With the data stored in Delta Lake, you can use Apache Spark to query and analyze the data using SQL and other programming languages. This can include running data analysis jobs, creating reports, and generating visualizations.

Monitor & Optimize Performance

Finally, you need to monitor and optimize the performance of the data lakehouse to ensure that it is scalable, reliable, and secure. This can include tuning Apache Spark and Delta Lake settings, implementing data governance policies, and optimizing data access performance.

Low-code lakehouse platforms provide a powerful tool for organizations looking to build data-driven applications and leverage the benefits of data lakehouses. It allows organizations to rapidly develop applications that can access and analyze data from a single, centralized data store, enabling faster and more effective decision-making.

Recommended Read: A Low-Code Lakehouse Guide by Prophecy.io.