Data lakes and data warehouses are critical technologies for business analysis, but the differences between the two can be confusing. How are they different? Is one more stable than the other? Which one is going to help your business the most? This article seeks to demystify these two systems for handling your data.
What is a Data Lake?
A data lake is a centralized repository designed to store all your structured and unstructured data. Further, a data lake can store any type of data in its native format, ignoring size limits. Data lakes were developed primarily to handle large volumes of data, and thus they excel at handling unstructured data. You typically move all the data into a data lake without transforming it. Each data element in a lake is assigned a unique identifier, and is extensively tagged so that you can later find it via a query. The benefit of this is that you never lose data: it can be available for extensive periods of time and it's very flexible because it need not adhere to a particular schema before it is stored.
What is a Data Warehouse?
A data warehouse is a large-capacity repository that sits on top of multiple databases. It is designed to store medium to large amounts of structured data for frequent and repeatable analysis. Typically, a data warehouse is used to bring together data from various structured sources for analysis, usually for business purposes. Some data warehouses can handle unstructured data, but this is not common. Work is involved to ensure that the data types are compatible before you can integrate the data. Because the data stored in a warehouse is structured, the size of the data is constrained, and the schema is determined before data can be added to the warehouse.
Data Lakes vs Data Warehouses
Picture a warehouse: there’s a limited amount of space, and the boxes must fit into a particular slot on the shelf. Each box needs to be stored in order so that you can later find it, and you will likely need to design the warehouse so that old inventory is purged periodically. Most of these same constraints apply to a data warehouse: the size is fixed, and each piece of data must be stored according to a schema that is carefully designed before you can add the data to the warehouse. Data warehouses are optimized for structured data.
By contrast, a data lake is amorphous, the boundaries can grow or shrink based on the contents. Like a lake, if more data is poured in, the data lake expands, and when data is removed it shrinks. The data does not need to be structured because you use extensive tagging to find the data when you need it. Data lakes are optimized for unstructured data.
The following table shows some of the key differences between data lakes and data warehouses.
|Data Lake||Data Warehouse|
|Storage||Data is unstructured, and all data is kept in its raw form. All data is stored, and it is only transformed when it is analyzed.||Data is typically extracted from transactional systems. The data is cleaned and transformed before you load it to the data warehouse.|
|Data capture||Captures semi-structured and unstructured data.||Captures structured data and organizes it in schemas.|
|Purpose||A data lake is ideal for deep analysis of unstructured data. For example, a data scientist might use advanced analytical tools with capabilities such as predictive modeling and statistical analysis.||A data warehouse is ideal for operational uses such as monthly reports, because it is highly structured.|
|Schema||Typically, the schema is defined after you store the data. This requires less initial work and provides more flexibility.||Typically, the schema is defined before you store data. This requires you to cleanse and normalize the data, and it means that the schema is far less flexible.|
|Better for…||Unstructured data, explorations, innovation, flexibility.||Structured data, high performance, repeatability, constant use.|
What’s right for you — a data lake or a data warehouse?
The simple answer is that you probably need both.
Data warehouses are ideal for the kind of repeatable reporting that is common in business practices, such as monthly sales reports, tracking of sales per region, or website traffic. A data lake is useful when you have a less straightforward analysis to perform. For example, maybe you want to perform behavior analysis of the traffic on your website. These are complementary rather than competing tools.
How Alooma can help
Regardless of whether you are moving data to a lake or a warehouse, Alooma can help you get your data in one place — which is a major challenge for both tools. Alooma is a cloud-based ETL solution designed to bring your data together.
Alooma can help you move your data to a lake or to a warehouse:
- Alooma is fast. If you are loading to a data warehouse — such as Amazon Redshift, Google BigQuery, Microsoft Azure, Snowflake, etc. — Alooma can extract, transform, and load your data in near real time, so you can make business decisions quickly. You can perform transformations on the fly and Alooma can even detect schemas for you, so more of the processing can happen in transit.
- Alooma is flexible. If you are loading to a data lake — like Amazon S3 — Alooma can handle the widest variety of data types, both structured and unstructured.
- Alooma is secure. Security is a cornerstone of Alooma’s offering. Alooma encrypts data in motion and at rest. In addition, Alooma is 100% SOC 2 Type II, ISO27001, HIPAA, and GDPR compliant.
- Alooma is cost-effective. Alooma has a team of experts and a secure, robust infrastructure. You can save your budget for the projects that matter most to your business.
Are you ready to see the Alooma difference? Contact us today to learn how we can help!