Businesses rely on data from various sources to run daily operations. The data comes from financial applications on-premises and in the cloud, IoT sensors, and social media. The data can be unstructured, like social media posts and videos, or structured, like transactions in relational databases.
Businesses capture high amounts of data and use them to drive critical decisions like product offerings and customer service. With the data generated expected to increase to over 463 exabytes daily by 2025, companies need data lakehouses to store and analyze data effectively.
Data lakes and data warehouses have been popular storage architectures for big data. However, introducing a data lakehouse provides a hybrid of data lakes’ and warehouses’ functions. For instance, a data lakehouse provides object storage for data with the flexible features of data lakes and data management functions of data warehouses.
Depending on your company’s data needs, you need to understand big data storage techniques to develop a robust pipeline for machine learning workloads, data analytics, and business intelligence.
Data Lakehouse: A Combined Approach
Data storage started with data warehouses that allowed users to archive data for specific business intelligence purposes and to report. Data warehouses date back to the 1980s and have served companies well before the introduction of big data.
With businesses looking for ways to work with unstructured data, a new storage architecture was invented called data lakes. While a data warehouse only stores structured information, data lakes can store information in raw format, and data scientists can analyze the data anytime.
While data lakes are effective, they have limitations. For instance, the schema in raw formats makes data lakes need to be extracted, loaded, and transformed (ELT) before analysis.
A data lakehouse offers solutions to the shortcomings of data warehouses and data lakes. The new storage architecture for big data provides a repository for data and enables the use of artificial intelligence and machine learning to analyze the information.
The data lakehouse architecture can store semi-structured and structured information like a data lake. That means the data from the warehouse feeds the business intelligence analytics while the raw data from the lake is used in data science.
Business users can access information quickly in a data lakehouse since the structure and schema are applied to unstructured data. Data lakehouse architecture enables smart analytics, perfect for many organizations collecting large forms of data.
Businesses can integrate new data sources without adjusting them to meet the organization’s data formats. You also don’t need data engineers since any tool can query the data.
The Benefits of a Data Lakehouse
Data lakehouse architecture is an affordable and flexible storage option that provides data management features. Some of the benefits of its implementation include the following:
• Reduced data redundancy
The modern data stack provides a multi-purpose platform that caters to all business data demands. Many organizations can avoid data duplication and save storage space. Since the architecture has a data mesh, it can handle multiple data sources.
• Supports a variety of workloads
A data lakehouse handles multiple workloads, from machine learning to SQL, advanced analytics, and data science. You have to incorporate multiple tools to support the workloads, but they use the lakehouse data repository. For instance, the Apache Spark engine is used to execute data science, machine learning, and data engineering functions.
• Ease of governance and data security
Since the data lakehouse structure enforces data integrity and schema, it is easier to implement governance mechanisms and robust data security.
Data Lakehouse vs Data Lake vs Data Warehouse: Which One Suits Your Needs?
A data warehouse is the oldest big data storage used in analytics, reporting, and business intelligence. However, the struggle with unstructured data can limit the information available to businesses. Data lakes emerged to handle raw data for machine learning and data science workloads.
On the other hand, a data lakehouse combines flexibility from data lakes and data management capability from the data warehouse.
The AICD compliance of the data lakehouse allows consistency as multiple users work on the data from the repository. Data lakehouses are effective since they can handle workloads related to machine learning and data analytics. A business can use technology appropriately for business intelligence and advanced analytics.
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