Data Warehouse vs. Data Lake

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. Technology innovators have developed a diverse range of platforms, but the distinctions between them can sometimes be confusing. Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences.

In this article, we’ll focus on a data lake vs. data warehouse. We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data.

Preprocessed Data vs. Raw Data

Data warehouses emerged several decades ago as a means of combining, harmonizing, and preprocessing data in preparation for advanced analytics. Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators.

A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model.

Data lakes, in contrast, are designed as repositories for all kinds of information, which might not initially be organized and structured. Data lakes are often used for situations in which an organization wishes to store information for possible future use. Stakeholders in the company might not have a clear use case in mind yet, but they want to retain the information in a repository where it can be readily available for analysis.

This is not to say that data lakes aren’t used to address current scenarios for business analytics, but rather, that they are well suited to storing raw data that has not necessarily been subjected to preprocessing.

Data lakes are often preferred for storing semi-structured data (such as XML files) and unstructured data (such as natural language text).

Many of the preferred platforms for analytics fall into one of these two categories. Snowflake, for example, is a SaaS-based data warehouse application that is ideally for storing large volumes of data in the cloud, making it available for analytics.

Other platforms defy simple categorization, however. Apache Hadoop, for example, was initially created as a mechanism for distributed storage of large amounts of information. It is often used as a foundation for enterprise data lakes. It lacks many of the important qualities of a traditional database such as ACID compliance. Hadoop is very good at storing and processing huge volumes of data, but it is not as well suited for ad hoc queries as some of the more traditional database or data warehouse platforms.

Business Users vs. Data Scientists

Another key difference between data lakes and data warehouses revolves around ease of use and typical use case scenarios. Users of a data warehouse generally have a clear idea of the data sets they’re interested in using. They are typically exploring data in the same ways, repeated over the course of time. A business manager, for example, may be interested in product sales by region, product line, and retail outlet. They may want to look at those numbers on a daily or weekly basis.

A second data warehouse scenario might involve periodic access to data with a well-defined objective in mind. Retail site selection, for example, might not be an everyday occurrence, but it will nevertheless entail a clearly defined process that analyzes specific data sets in specific ways. In this respect, data warehouses tend to be well suited to the needs of a wide range of business users.

A data lake, in contrast, lends itself to novel use cases. Certainly, they can be used for more routine analytics, but their application extends much further than that. Because data lakes are used to collect and store large volumes of unorganized data, they provide a means of bringing together information from various original sources, organizing after the fact, and developing specific analytics scenarios as required.

This process calls for a higher level of expertise, so in this respect, data lakes more frequently require the help of data scientists to turn raw data into meaningful insights.

Snowflake, for example, is a cloud-based data warehouse, so it does an extraordinarily good job of organizing and preprocessing data for core analytics. Hadoop, which is well suited for data lake applications, can also provide routine analytics, but it is especially strong at gathering vast amounts of data, without any concern for the type of information it’s collecting.

Flexibility

Data lakes are, by their very nature, designed with flexibility in mind. Because they lack the kind of organized structure inherent in relational databases and data warehouses, they can be changed relatively easily. They are malleable.

Data warehouses, in contrast, always conform to a specific structure or model. They can be changed, but not easily. Modifications to the existing structures and relationships in a data warehouse can have far-reaching implications, so they should not be undertaken lightly.

Hadoop and Snowflake represent tremendous advances in analytics capabilities. The same can be said of other leading platforms such as Databricks, Cloudera, and data lakes offered by the major cloud providers such as AWS, Google, and Microsoft Azure.

Whichever platform you choose, Precisely Connect can help you integrate data from any source, including the critical mainframe systems like IBM i, z/OS, and others. Organizations that rely on mainframes to process business-critical data can bring all of their information together in one location with complete confidence in the integrity of the process.

Precisely helps enterprises manage the integrity of their data. Data governance and data quality, data integration, location intelligence, and data enrichment provide a foundation for trustworthy insights to drive powerful business results.

To learn more about a data warehouse vs. data lake and the importance of choosing the right integration tools, read our eBook A Data Integrator’s Guide to Successful Big Data Projects.