Scuba Tech Library

Data Lakes and Data Warehouses -- Which Is Right For You?

Data lakes and data warehouses are both commonly used for storing data, but there are key differences between the two that make them unique in their own way. Learn which fits your business purposes best and if there is a better solution.

Data Lakes

Like a container, a data lake acts as a storage repository for large volumes of all kinds of data. Because data lakes are vast pools of raw data, the purpose of the data is usually yet to be defined. The benefit of data lakes is that your teams can collect whatever data they want (or can), and it’s easily saved without having to structure the data sets.

The Challenges of a Data Lake

Because data lakes are filled with raw data, it can make querying it difficult, because:

  • You don’t know what is in it
  • You also have to sift through things you don’t want to find the things that you do want

Finding answers to your data questions with a data lake usually requires the assistance of data science teams.

Data Structure

  • Raw data makes it harder to decipher for users without technical knowledge. For instance, if a business analyst wants to understand sales performance, they might not know where to begin without data scientists who know their way around raw data.
The Strain of Large Data
  • Unprocessed data creates a large volume of sometimes unwanted data, leading to long query times
  • Users find it difficult to extract the most valuable data from the repository

Data Governance

  • Data lakes are hard to govern due to inconsistencies that hinder combining various data sets

Data Warehouse

A data warehouse is a repository for structured data, meaning the exact tables and columns are already known. Usually, this indicates that the filtered data has already been processed for a specific purpose.

Data warehouses are much easier to query against (compared to data lakes), only requiring straightforward SQL.

The downside of data warehouses is that what you can query is limited in scope. If you want to add or drop a column or change a format of things you’ll need extract transform and load (ETL) work to re-state the data to exactly what you want, which can be a tedious process.

Data Lakes vs. Data Warehouses

Some of the major differences between a data lake and a data warehouse are:

Purpose

  • In data lakes, the data is in raw format, and often, its individual purpose is not determined before it eventually becomes processed or structured.
  • Data warehouses only have processed data. Each table created from a data warehouse is created with a specific purpose, which translates into limited scope.
Data Structure
  • One of the biggest differences between the two is that data lakes store unprocessed and raw data, while warehouses store refined and processed data. As a result, data lakes require more storage capacity than data warehouses.
  • Data in data lakes is malleable, which makes it particularly helpful for machine learning.
  • Data warehouses can save costs by not storing data that may be useless.

Accessibility

  • Since there is no structure in a data lake, it’s more challenging to find what you’re looking for. For example, in a data warehouse, you might already know that you have finance, marketing, and product data sets, and can easily ask specific questions about each silo. Conversely, if all the contents of those tables are stored in a data lake (in an unstructured raw format), it’s challenging to discern what's important and what isn't. Your data will be noisier, with unnecessary columns making it harder to distill only information about a particular silo.
  • Alternatively, there is a better architectural structure of data warehouses, which makes data deciphering easier. However, it is costly and harder to bring change to data warehouses due to more structural limitations.

Users

  • With the raw data in data lakes, only specialized tools and scientists can make sense of it for business use.
  • With the processed data in data warehouses, you’re really limited to what is in the structured tables. For example, if you have a structured marketing data table with columns A, B, and C and you can use those regularly as a marketer. But if you have a question about something from a column that’s not in that structured table, and you don’t know SQL, you’ll need a data scientist to restructure the table to include the necessary information.
The Happy Medium

As data preparation, analysis, and visualization tools are gaining popularity, end-users now have more options for self-service access to information whether they are stored in a data lake or data warehouse.

For example, you can use Scuba Analytics as a data lake or data warehouse. With Scuba:

  • You can store anything in the database (like a data lake), and don’t have to leave anything out.
  • Querying the Scuba "data lake" is very easy. You can use our UI to build queries, without code like SQL.
  • Sift through an easy-to-use and intuitive data dictionary which is automatically created by Scuba
  • Easily add or remove columns as you wish, without having to re-state or rebuild the whole table

With Scuba, you can import anything into a single data set/table, and add or drop columns as you need them, which is fundamentally how a data lake works. Then, Scuba Analytics can use RBAC (role-based access controls) to surface the columns that matter for specific silos without having to rebuild the table, which is inspired by the functionality of a data warehouse.

Unlike data lakes, which are hard to access and are poorly organized, Scuba is a clean partition of everything, making it easy to access your information, like you would in a data warehouse. And, there is no SQL or code needed at all.

What to look for in the future with Scuba Analytics

Scuba Analytics is also building features to automate the table building process, so you need very little context as to what is in the data itself, and Scuba can pump out the queries you need the most, instantly. This means that when you enter a dataset into Scuba, even if it comes with zero context, we will generate highly valuable queries and dashboards automatically.

You don’t have to do anything and you’ll still receive valuable insights about the customer's behavior that matters most to you!

Want to learn more? Request your free Scuba Analytics Demo today!

REQUEST A DEMO

Data Science

What is a Data Ecosystem?

The term “data ecosystem” collectively refers to all the programming languages, algorithms, applications, and the general infrastructure used to collect, analyze and store data.

Data Science

What is Data Modeling

Data modeling is a means of creating a conceptual framework for your data in preparation for storage in a data warehouse. The resulting model is a visual representation of the data which maps out the relationships between data, and the rules.

Data Science

What is Lambda Architecture?

Lambda architecture processes data through a hybrid combination of batch processing and stream processing.

Data Science

What is an ETL Pipeline?

ETL is a method to collect raw data from various sources, clean it up, and translate it so it can be used to inform decision making.

Data Science

What is Data Governance?

Data governance allows organizations to ensure high-quality data through formalized processes for management, monitoring, and control of data assets.

Data Science

What is Hadoop?

Apache Hadoop is one of the most widely used open source frameworks designed to address the problem of storing and processing big data.

Data Science

What is Parquet?

Unlike row-based formats such as CSV, Parquet is a columnar data file storage format.

Data Science

What is Querying?

A query is a question or request for a database written in a code the database can understand, in order to retrieve or modify the correct information.

Data Science

What is an Ad Hoc Query?

An ad hoc query is any kind of question you can ask a data system off the top of your head.

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