Every data-driven enterprise looking to get the most out of its data is continuously searching for a cost-effective, scalable, high-performance storage and analytics architectural solution.

Organizations have moved from traditional data warehouses to data lakes and are now shifting to data lakehouses, which combine the best features of the data lake and data warehouse. Popular lakehouse architectures include Databricks Deltalake, the AWS data lakehouse, Azure data lakehouse, and Oracle data lakehouse.

As an upgrade from transactional databases, adopting data warehouse tools as a storage, query and reporting tool significantly improved the speed of ACID (atomicity, consistency, isolation, and durability) transactions and data quality while ensuring proper data management and governance.

Then came the evolution of big data. Big data brought a lot of variety and velocity into business data, which put the capabilities of data warehouses to the test. They were not only expensive to set up, but they could only cater to structured data – a real problem since around 80-90% of big data is unstructured or semi-structured.

So, data lakes entered the picture. Data lakes are cost-effective storage solutions capable of handling massive volumes of data in different formats for use in various business use cases like data science, analytics, and machine learning. However, as with the risks involved in creating a data solution that caters to different schemas and formats, there was the risk of developing a data swamp. Additionally, data lakes didn’t ensure data quality and management and couldn’t perform ACID transactions on data.

Organizations started employing a data lake and a data warehouse or solution like Apache Impala to get the best of both solutions, creating a more complex architecture and more challenging data management. This practice also meant moving data between multiple systems, which meant more data movement, resulting in data redundancy and low-quality data.

It was solving this complexity that led to the development of the data lakehouse. The data lakehouse combines the flexibility and economical cost of storage of data lakes with the data management and fast ACID transactional ability of data warehouses, giving rise to a layered data storage architecture that serves most data needs today.

A data lakehouse is an open data management system that combines the best features of a data warehouse and a data lake, giving rise to a robust solution with the following characteristics

• Economical cost of storage: Data lakehouses build on the low-cost storage ability of data lakes and can scale to increasing volumes of data, a vital aspect for large organizations today.
• Flexibility in handling numerous data types (structured, semi-structured, and unstructured): In an era where most business data come in unstructured formats, data lakehouses can work with various data types (structured, unstructured, and semi-structured) and formats, all while being able to interact with standard SQL vs. ANSI-SQL.
• Data analytics, machine learning, and data science application: Data lakehouses provide an ideal solution for implementing diverse workload needs, such as data science, Machine Learning (ML), and Business Intelligence (BI) reporting as they expose access to Apache Spark.
• Simplified schema and easy scaling: Data lakehouses implement the simplified schema of data warehouses, like defining typed tables with constraints and data types on unstructured data.
• Data management and governance: Ensuring data quality and governance is critical, and data lakehouses have these features built in. Additionally, by combining all data storage and analysis needs into a single system, data professionals only need to maintain one system, resulting in easier data management.
• Faster time to data access and increased data sharing: The existence of all organizational data in one system reduces data movement across multiple systems, the risk of data redundancy, and gives rise to more quality data for analysis.

Data lakehouses present the best of both worlds—data lakes and data warehouses, and hence use a different approach to handling business use cases. Here are some applications of data lakehouses and how their architectural applications differ from data warehouses and data lakes.

1. Lake-first approach to Data Analytics — Organizations can trust and make confident decisions based on results obtained from data lakehouses. Unlike the data warehousing approach that contains outdated data (months or weeks), lakehouses approach analytics using the lake first approach. Adapting a lake-first approach allows organizations to access the most recent and freshest data for use in Business Intelligence (BI) reporting and insights. In this way, organizations make decisions based on the most recent data, giving rise to more informed decisions.
2. Self-service Data Experience — With the adoption of data lakehouses by businesses, organizations can let users have faster access to data for use in creating value. By harnessing the power of high-performance queries, more accessible data access, flexibility and application of data management principles, businesses give users the power to perform tasks without requiring proficient technical ability. Additionally, with the performance optimization capabilities of data warehouses, users have more confidence in the quality of their data results.
3. Single Cloud Data Lakehouse Management — Combining the capabilities of the data warehouse and data lake simplifies the data landscape, and data professionals need not move data frequently between multiple systems. Instead, all data management activities occur within a single system, requiring less effort, time, and easier management. An example is Azure Databricks which is compatible with multiple Azure solutions like Azure Data Lake, Azure Synapse Analytics, and Azure Machine Learning. Azure Databricks helps unify the various workloads, making it easy for data professionals to operate and manage the entire cloud data lakehouse architecture.
4. Layered Data Architecture — Most organizations employing a data lakehouse solution use a three-layered approach to handling their data from arrival to end use case. These layers are the raw, curated, and final layers. The raw layer represents the entry point whereby data ingestion occurs and helps create other downstream areas. The curated layer holds the cleaned, transformed and refined data and forms the foundation for use in the final business layer. The final layer exists to serve business needs like BI reporting, data science, and ML. This layered data architecture helps improve data quality because validation and various ETL processes occur before they enter the curated layer.
5. Open Interfaces and Data Formats — Data lakehouses utilize open storage formats like Parquet, an open source file format for easy storage and retrieval, which allows easy interoperability among different systems. Hence, organizations can quickly build their data lakehouses by leveraging these tools. Furthermore, utilizing open data formats means no data lock-ins within an organization, as there is a free exchange of data where rules occur by using an open decision-making process.

The StreamSets modern data integration platform provides more visibility and reliability into an organization’s data lakehouse environment and helps companies manage their data lakehouses more effectively. In addition, by utilizing her user-friendly interface, organizations can build reusable data pipelines to feed data into their data lakes and use that for creating their cloud lakehouse architecture.