Data Lake vs. Data Warehouse: Differences and Similarities

The terms “Data Warehouse” and “Data Lake” may have confused you, and you have some questions. Are these two terms used to describe the same thing? In the event that they are not the same, what are the differences? Is there a time when one should be preferred over the other? This article will provide answers to these questions and more. 

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. Essentially, this is the difference between a lake and a warehouse. The data lakes store data from a wide variety of sources, including IoT devices, real-time social media streams, user data, and web application transactions. There are times when the data is structured, but it is often messy since it is ingested directly from the data source. On the other hand, a data warehouse contains historical data that has been cleaned and arranged.  

What is Data Warehouse? 

Built to make strategic use of data, a Data Warehouse is a combination of technologies and components. A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. To provide meaningful business insights, it collects and manages data from a variety of sources. An electronic database consists of a large amount of information that can be queried and analyzed rather than processed for transactions. In other words, it is the process of converting data into information.   

Data Warehouse in DBMS:  

A data warehouse is a form of a data management system that enables and supports business intelligence (BI) activities, particularly analytics. Data warehouses exist solely to perform computations on enormous volumes of historical data. 

Data warehouses include the following examples: 

  • Amazon Redshift. 
  • Big Query by Google. 
  • Db2 Warehouse by IBM. 
  • Synapse on Microsoft Azure. 
  • Autonomous data warehouse from Oracle. 
  • The Snowflake database. 
  • The Teradata Vantage system. 

What is Data Lake? 

Essentially, a data lake is a repository of raw data from disparate sources. A data lake stores current and historical data similar to a data warehouse. In addition to JSON, BSON, CSV, TSV, Avro, ORC, and Parquet, data lakes can store data in a variety of formats. Typically, data lakes are used to analyze data. 

Data lakes, however, are sometimes used as cheap storage with the expectation that they are used for analytics. For building data lakes, the following technologies provide flexible and scalable data lake storage: 

  • Amazon Web Services S3 
  • Gen 2 Azure Data Lake Storage 
  • Cloud storage provided by Google 

Data lakes can also be organized and queried using other technologies, such as 

  • Atlas Data Lake powered by MongoDB. 
  • Athena on AWS. 
  • Presto. 
  • The starburst. 
  • Analytics powered by Databricks. 

Data Lake Architecture Diagram 

The process of adding new data elements to a data warehouse involves changing the design, implementing, or refactoring structured storage for the data. It also includes implementing or refactoring the ETL processes for loading the data since data that enters data warehouses requires a rigorous governance process. 

This process may require a substantial amount of time and resources when dealing with a large amount of data. As a result, a data lake concept becomes a game-changer in the field of big data management. 

Data lakes emerged in the 2010s, suggesting that all of an enterprise’s structured, unstructured, and semi-structured data should be stored together in a single location. In a Data Lake architecture, Apache Hadoop is an example of a data infrastructure that is capable of storing and processing large amounts of structured and unstructured data. 

Data Lake Vs. Data Warehouse: Latest Industry Stats 

The Global Data-Warehouse-as-a-Service Market is expected to be worth USD 2,311.51 million in 2022, and USD 4,429.92 million by 2027, at a compound annual growth rate (CAGR) of 13.85%. The Data Lakes Market was worth USD 3.74 billion in 2020 and is predicted to be worth USD 17.60 billion by 2026, growing at a CAGR of 29.9% between 2021 and 2026. 

Data Lake vs. Data Warehouse: Similarities 

  • Data is stored in both a database and a data warehouse. These are systems for storing data. 
  • As a general rule, the bottom tier of a data warehouse is a relational database system. A database is also a relational database system. Rows and columns make up a relational database system, and a large amount of data is stored in it. 
  • The DW and databases support multi-user access. Many users can access a single instance of a database and data warehouse at the same time. 
  • In order to access the data, both the DW and the database require queries. Complex queries can be used to access the data warehouse, whereas simpler queries can be used to access the OLTP database. 
  • Databases and data warehouses may be located on-premises or in the cloud. 
  • It is also possible to refer to a data warehouse as a database. 

Data Lake vs. Data Warehouse: Differences 

In comparison to a data warehouse, data lake processing is different in the following ways: 

Different Storage Options 

  • The data lake keeps all data, no matter where it comes from or how it’s structured. Data is kept in its .raw format. Only when it’s time to use it is it transformed. 
  • In a data warehouse, structured data is prepared for strategic analysis based on predefined business requirements after cleaning and processing. 

Flexibility 

  •  It is imperative to note that accessibility and ease of use refer to the use of the data repository as a whole and not to individual data files. There is no structure to the data lake architecture, making it easy to access and modify. As a result, any changes that are made to the data can be made very quickly since data lakes have few limitations. 
  •  By design, data warehouses are more structured. It is important to emphasize that one of the major benefits of data warehouse architecture is that the processing and structure of data make the actual data itself easier to understand. However, the limitations of structure make it difficult and costly to manipulate data warehouses. 

Schema-on-Read Access 

  • Unlike a data warehouse, the schema is defined once the data has been captured and stored. This simplifies the process of capturing and storing the data.  
  • Data warehouses define their schemas before storing data. The processing of the data takes longer, but once it is completed, the organization can use the data with confidence and consistency. 

Decoupled Storage and Compute 

  • The storage requirements can be tailored to the frequency of access by separating storage from the computation. You can archive raw data at a lower cost tier, and access transformed, analytics-ready data more quickly by separating raw and transformed data.  
  • Using new technologies makes it much easier to conduct experiments and exploratory analyses. 
  • The tight coupling of storage and computation in traditional data warehouses and ETL servers means that if I need to expand storage capacity, I will also need to expand computation capacity. 

      Users 

  • People who are unfamiliar with unprocessed data often find it difficult to navigate data lakes. Usually, raw, unstructured data needs to be analyzed and translated by a data scientist using specialized tools. 
  • Data preparation tools are becoming more popular because they enable employees to self-serve access to information stored in data lakes. Processed data can be used in charts, spreadsheets, tables, and more, making it accessible to most employees, if not all. As with data warehouses, processed data only requires the user to be familiar with the topic represented. 

Tasks Performed 

  • Data engineers use data lakes to store incoming data. However, data lakes aren’t only limited to data lake storage. Remember, unstructured data is more flexible and scalable, which is often better for big data analytics. Apache Spark and Hadoop can be used for big data analytics on data lakes.  
  •  As training data increases, deep learning requires scalability. Typically, data warehouses are set to read-only for analysts, who primarily read and aggregate data. It is not necessary to insert or update data since it is already clean and archival. 

Conclusion 

 

We hope our blog will come to your rescue when choosing between a data lake and a data warehouse. Occasionally, a combination of both storage solutions may be necessary. The importance of building data pipelines cannot be overstated.

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