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Discover what a data warehouse and data lake are, how they compare, and when to use each one in this comprehensive data solution guide.
Big data analytics help organizations use data to explore both new and improvement opportunities. Whichever cloud data platform you choose, there are two data storage technologies you will want to understand.
Data warehouses and data lakes are the two dominant data solutions commonly used for defining how an organization stores, queries, analyzes, and reports on big data.
This post will define what a data warehouse and data lake are, how they work, and their differences. By the end, you’ll have enough information to decide which data solution to go with for your big data strategy.
Table Of Contents
You will probably only ever deal with four types of data, whether you are a data specialist or CTO; structured, semi-structured, unstructured, and metadata.
Structured data refers to stored data in a standardized format, such as rows and columns, to be more easily understood. You can store, retrieve, and analyze it for specific purposes for that reason.
Examples of structured data include SQL databases and Excel files.
Unstructured data is not organized as well and does not work within a defined data model. This makes the data not immediately ready for use unless you dig into it for a specific reason.
Examples of unstructured data include no-SQL databases, audio, video, PDF documents, and images.
Semi-structured data is not merely a combination of modeled and non-modeled data. Contrary, it is the type of data that does not follow most data structures but uses tags or markers to define elements, fields, and records within itself.
XML and JSON are two examples of semi-structured data.
An increasing number of tools can help your organization query semi-structured data, such as Snowflake.
CloudZero provides Snowflake cost intelligence so you can understand your costs at every level of querying semi-structured data.
Metadata is the type of data that describes other, specific data. Seem confusing?
Think of shooting a video with your smartphone camera. The phone saves the footage with additional information that is typically easy to understand, such as the date, time, and, sometimes, shooting location. Those details are examples of metadata.
Next, to understand how data warehousing and data lakes work, you’ll need to first tell how a database works.
A database is an electronic repository for structured data from a single source where you can store, retrieve, and query it for a specific purpose. There are proprietary and open-source databases, many of which are relational databases. Relational databases get their name from requiring schemas.
Schemas are a framework of structuring data to recognize and interpret patterns in that data. So relational databases are designed to work with structured data, coming from a single source — not raw data that varies in structure, format, and sources.
A data lake is a large repository that houses structured, semi-structured, and unstructured data from multiple sources. A data lake also contains both raw data and information (processed data). It is truly a lake of data where all kinds of rivers (data types) converge.
Yet data lakes differ from data swamps.
A data swamp is a vast repository with little to no structure, making it unusable or of little use to data specialists.
A data lake is especially useful for storing all kinds of data, whether you need to analyze and report all or bits of it immediately or in the future. Data lakes are also an excellent feeding ground for big data, artificial intelligence, and machine learning programs.
However, data lakes can be tough to derive insights for everyday business needs unless you are a data specialist. This is where other types of standardized data storing options come in.
Here are some of the best data lake solutions in the market right now.
A highlight of the data lake on AWS is it is simpler to handle than most alternatives. The AWS Lake Formation service makes setting up a secure data lake quite accessible.
Credit: AWS Lake Formation
Another benefit is integrating AWS’ other solutions, such as machine learning services Amazon Redshift and Amazon EMR (for Apache Spark), with an Amazon S3 data lake that promotes convenience, data security, and centralization benefits.
Another big player, Azure’s big advantage is its ability to scale to meet the most demanding workloads, and maintaining top performance while at it. The Azure Data Lake Storage option is also viable for its compatibility with many other data storage and querying frameworks.
This data lake tool is ideal if you want to harness more value from a Hadoop-based data lake. The underlying Hadoop architecture means you do not have to do much coding to query colossal amounts of data. Still, it supports other data tools such as Amazon Aurora, Microsoft Azure SQL Database, AWS Redshift, and Microsoft SQL Data Warehouse.
A data warehouse is a relational database that can handle, store, and bring to one place structured data sets coming from multiple sources. Data warehousing supports business decision-making by analyzing varied data sources and reporting them in an informational format.
Think of the different data sources as the various departments in your organization depositing organized data in one place. The goal is usually to help provide practical insights into an organization’s multiple operations.
Unlike a primary database, a data warehouse can handle exabytes of data and usually start at one terabyte capacities.
Many organizations prefer to make large amounts of data accessible to employees by using a further subset of data sets known as data marts.
Snowflake and Amazon Redshift are some common data warehousing tools. Other top cloud data warehouse solution providers include Google BigQuery, Teradata Vantage, Oracle Autonomous Warehouse, Vertica, Microsoft Azure Synapse, Yellowbrick Data, and IBM Db2 Warehouse.
Still, some modern data solutions use a data lake architecture that can also act as a data warehouse solution.
Take Snowflake, for example.
Your organization can use Snowflake as data lake to derive the benefits of a highly scalable and cost-efficient repository for all data types and sources with the business-ready insights of data warehousing and cloud storage. All in one place.
Alternatively, you can use a separate data lake, only employing Snowflake as a data warehouse solution for analytics and transforming your operations’ data.
Data marts are databases that hold a limited amount of structured data for one purpose in a single line of business.
Here is an example. A data mart can be a database of organized data for your sales and marketing department that does not exceed 100 Gigabytes (GB).
The data in a mart usually comes from a data warehouse, which makes marts widely considered a subset of data warehousing.
Some similarities between data lakes and data warehouses include:
But these two have more differences than they have similarities.
The most significant difference is that while data lakes hold all manner of data, processed or not, data warehouses keep only structured data. Data lakes also keep the data in a flat architecture instead of the structured database environment in a data warehouse.
Data warehousing focuses on transforming raw data into information that businesses can use for decision-making.
Warehouse data is the core of business intelligence, relying on data analysis and reporting techniques to derive meaningful insights from operations’ data.
Instead, data lakes form the core of Big data, AI, and ML applications for the vast amounts of data they hold from multiple sources.
Data lakes are not as accessible to employees as they are to data specialists. One reason is traditional data processors do not render the data a lake contains in a way most people can understand.
But, the data in lakes does not demand as many compute resources as it takes to organize warehouse data. So data specialists find data lakes easy to access. That also makes data lakes cost-friendlier for storing vast amounts of data than data warehouses.
Data specialists can also decide when and how to model the data collected in a lake. So they can prioritize which data goes through analysis first to save costs. They can also collect data as they come up with new data modeling ideas.
If your organization produces mountains of data that you do not need to transform into insights right away, a data lake can be a good option.
But you would still need to translate that raw data into valuable and understandable information to remove the guesswork out of your decision-making. That’s where data warehousing comes in.
While data lakes are the most scalable in terms of data holding capacity, a modern data warehouse can handle incredible amounts of data ready to transform it into business intelligence on-demand.
Data lake and data warehousing are not direct competitors. They are not designed to be alternatives. They complement each other. Data lakes empower data warehouses and vice versa.
That means you’ll want to look into picking the best data lake solution along with a top data warehouse solution.
The primary difference between a data warehouse and a data lake is while the former is a macroscale repository for various types of data and structures, the latter holds colossal and organized data in a structured database environment.
Data lakes are ideal for organizations that have data specialists who can handle data mining and analysis. Additionally, they are suitable for organizations that want to automate pattern identification in their data using big data technologies such as machine learning and artificial intelligence.
Data lakes also help keep far-reaching data you do not need to transform right away or lack the resources to analyze immediately. Think of a data lake as a scalable online archive. On the other hand, a data warehouse makes identifying patterns in your operations so easy, anyone with some knowledge of the topic can tell what it means.
But processing raw data to that point takes a significant investment, from the right skills and experience to having a deep understanding of the best use cases for each data storage technology.
This is why high-performing engineering teams use CloudZero. CloudZero helps combine AWS data and Snowflake into rich cost insights you can use to analyze, monitor, and optimize your cloud spend. With a holistic view of your costs across AWS and Snowflake, your engineering teams can make informed decisions to better optimize your product or features for profitability.
Learn more about our Snowflake Cost Intelligence here and how it can help your team gain a more complete view of your cloud costs. to see it in action!
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