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Data lake or data warehouse? This depends on the type of data and your business objectives. Your organization will likely need both solutions.
What is a Data Lake?
Like a data warehouse, a data lake is a repository of data from different sources. In a data lake, you can save structured, semi-structured, and unstructured data in their raw form. Data does not need to be structured in a data store and can remain in its original format.
Data can be stored faster in its raw format, making it possible to store real-time data. It is also easier to maintain. The information is structured only when needed, so special skills and tools are required to analyze and gain insight from the data.
While most business users are able to get a report out of a data warehouse for their needs, you may need a Data Scientist in order to do the exact same thing with a lake. While you can save money by storing the data, it may cost more to keep specialists around in order to make that data useful. But the results may be more valuable because they are less expected.
Data lakes are storage environments for structured, unstructured, and semi-structured information sourced from business applications and relational and non relational databases.
The data lake's name is apt because it allows those who deal with big data - data scientists, analysts, and engineers - to experiment and dive deep into all the internal and external information that an organization can channel into the lake. This includes data from its data warehouses.
Data lakes provide access to diverse data sets in different formats, such as log files, financial reports, and other data, for data analytics. These solutions are perfect for processes like machine learning and predictive analytics that require vast amounts of data.
Data Lake Diagram
In order to extract business intelligence from raw data sources within a Data Lake, the data will need to be processed using advanced analytics tools and then transferred into another environment such as a warehouse or data mart. The data can be accessed by users of data visualization tools and business systems, like high-performance databases.
This diagram gives a high-level overview of the process.
Data Mart and Data Lake
What is a data mart? Data marts, like data lakes, are systems that store data. However, data marts hold more processed data. Data marts consist of modern databases that contain a small amount of structured data for a particular business function.
Data Lake Example: Cloud and On-Premises
Here is the data lake example Two ways are available to implement a data lake: either in the cloud or on-premises.
You'll be working with a provider - AWS, Microsoft Azure, and Google, for example - who will host your cloud data lake and handle all the details, such as managing security and backing your data. Cloud data lakes are accessible via the Internet, and providers will likely charge you a monthly fee for their services. Cloud data lakes are popular because they require less work and let businesses focus on their data.
Data lakes on-premises are more difficult to implement since companies must purchase and install the necessary hardware and software for them to be set up and maintained. The companies will also have to hire specialists to ensure the data lake is secure and performs optimally. On-premises data lake considerations include power and space requirements. In short, this approach is often very resource-intensive--which is why many organizations today head straight for the cloud platform when they need to create a data lake.
What is a Data Warehouse
To compare a data lake with a warehouse, let's quickly define the latter. A business warehouse is a repository that integrates and stores structured data (like spreadsheet data) from multiple sources within an organization.
Let's first define a database.
A database is an information repository that stores data from a single, centralized source. The data can be generated by one application or information about a specific department within an organization. Data in traditional databases can be structured to make it easier for analysts to find and analyze certain information. However, the data must first be processed according to a specific schema before they can store it. A data warehouse can be compared to a huge database. Data warehouses are a collection of disparate data sources that have been unified and optimized. This is ideal for data analysis as well as business intelligence.
Cloud-based data warehouses have become the preferred choice. The cloud services are a great way to store big data, as it allows for scalability while lowering costs/cost saving.
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Data Warehouse Diagram
The Extract, Transform, and Load data integration process is used to integrate information from different sources. Data warehouses can be built from transactional systems, relational databases, and data lakes. Data warehouses are used by businesses to generate business intelligence (BI), data visualizations, and reporting using high-speed SQL queries.
Data Warehouse Example
Data warehouses can be classified into the following types:
- Enterprise Data Warehouse (EDW), centralizes the data of an organization and makes it available to all those who need it for analysis and reporting. An EDW may include one or multiple databases.
- Operational Data Store (ODS), that integrates data from different sources. ODSs are primarily used for querying data that is frequently refreshed in real-time.
A data mart is another type of data warehouse, which was also mentioned in the article. Data marts are considered to be a subset of a data warehouse. Data marts, which are smaller than 100GB and designed to be used by a specific business unit like the marketing department, are more compact. A data warehouse, on the other hand, can be used by an entire company and is larger than 1TB.
Data lake vs. Warehouse
It is essential to know the differences between data lakes and data warehouses in order to choose the right solution for you. Let's look at what each solution offers.
Data Types
Data warehouses can store unstructured data. Data lakes are able to store unstructured information. If you want to store unstructured data, such as text, images, sensor information from IoT devices, or server logs, a data lake is the best option. It is possible but more difficult with a data store.
Data storage
The approach to accepting data for a warehouse is different than it is for a lake. Data warehouses define the schema of the data in advance and only accept data that can serve a particular purpose. This is the "schema-on-write" method.
Data lakes are a new approach that uses "schema-on-read." This means that all data, regardless of its potential usefulness, is accepted. A schema can only be created when the request for data is made.
Different approaches will also determine the time when computing resources are most used. For data warehouses, it's when data is being stored. For data lakes, it's when data is processed to answer a query.
Costs
The cost of maintaining data lakes and warehouses is directly affected by the way they store their data. The setup process for data warehouses is more costly because it requires a large number of decisions to be made and a great deal of preparation before you are able to use the storage. Data models are needed to determine the type of data that should be stored in a data warehouse and how to organize it to allow for quick reporting and analysis. It is true that not all data will be preserved. However, this reduces costs for disk storage.
It may seem that a data lake is expensive because it can be measured in petabytes. Since there is no processing of the data prior to its storage in the lake, it is much cheaper and easier to use the hardware needed for storage.
Access to the data lake also helps to save costs because you're not restricted to predefined reports to explore raw data. Data warehouses are designed to offer quick data analytics and reports, but if you're interested in asking new questions, you will have to wait until the developers implement design changes.
Users
A data warehouse takes time to build, but once it's done, anyone can create a report that includes specific parameters from a large dataset. It is especially useful for members of the Operations team who are interested in key metrics and visualizing data. Data that has been unified and analyzed from multiple sources can help business analysts uncover trends.
This is also possible with a data lake, but sorting it out is a difficult task because the data is not standardized beforehand. Users will need special skills and/or tools to be able to find the answers, as they'll have to search through metadata instead of clearly-structured tables. Raw data, which has not been pre-processed, allows data analysts the freedom to dig deeper into business insights. Machine learning can also benefit from data in different formats and multiple sources.
Read More: Relationship Between Business Intelligence Solutions And Warehouse Relationship
The use of Technology
Data warehouses have historically been the same as relational databases. They allow faster processing of queries, but they are more expensive to update in real time with data. Modern cloud data warehouses are different. ELT (extract-transform-load) processes replace traditional ETL pipelines.
Cloud-based data warehouses are more scalable and agile. Special tools for managing the data (like Google Big Query and Azure Synapse Analytics) also save time and money.
Data lakes are gaining in popularity due to the widespread adoption of big data and cloud computing technologies, such as the open-source Hadoop system. This is a scalable and easily adaptable distributed file system. There are other cloud-based data lake management options available from major service providers. For example, Google Cloud Storage and Azure Data Lake.
Data Warehouse vs. Database
What is the main difference between a database and a data warehouse? Although a data warehouse is technically a database, its purpose is to allow organizations to perform analytics using the data contained within.
It is not designed to be used for analytics. A database stores data in one place and can only be used to perform simple queries.
Data Lake vs. Data Warehouse: How data is stored?
The ETL process is used to store data in a warehouse. The data is extracted from different sources, transformed (cleansed, converted, and formatted to be usable), and then loaded into the warehouse, where it is stored hierarchically as files and folders.
The flat architecture of data lakes allows them to receive data from a variety of sources, both internal and external, such as social media, mobile apps, smart sensors, websites, and many others. These data are stored in the form of files or objects by Data Lakes. Data lakes are discrete objects that can be accessed by using unique keys or identifiers.
Data Lake vs. Data Warehouse: How data is accessed?
Open-source frameworks such as Apache Hadoop, Apache Spark, and other frameworks and tools provided by commercial vendors are designed to process and analyze large data sets.
Users can access data warehouses using BI tools, dashboards, and applications. Direct SQL access can also be used to connect directly to the data and run queries.
How do Data Lakes and Warehouses work together?
Data lakes and warehouses are complementary and often coexist in an organization's infrastructure for data, including the cloud. A data lake allows the business to experiment with data, gain insights and then transform it into a system that is more suitable for the organization.
Can Data Lakes Replace Data Warehouses?
Data lakes are testing grounds for data analysts, data scientists, and data developers. They allow them to explore the potential of data to deliver insights to businesses. Data lakes are also used to support data-intensive tasks like the training of artificial intelligence (AI).
How To Choose Between A Data Lake And A Data Warehouse?
In the end, many organizations that are weighing the pros and cons of a data lake or data warehouse will likely find they don't need to make a choice. They may not be ready to work with unstructured data or experiment with new technologies like machine learning today, but they will in the future.
Even if your company isn't ready to manage and set up both solutions, it's important to know the differences between a Data Lake and a Data Warehouse and when they should be used:
How to use a Data Lake?
It's a smart move to store data in a lake if your organization collects huge amounts of data from multiple sources in different formats. You don't have to query or access the data right away. This is a more cost-effective solution than processing the data and storing it in a data store (if this solution can accept the data types that you wish to store). A data lake is also a good option if you're looking to experiment and test your data for complex data-intensive processes such as AI.
How to use a data warehouse?
A data warehouse is a logical solution for storing data if you want to make better and more accurate business decisions. Data warehouses allow you to consolidate data from multiple sources (including historical data) and get answers quickly using predefined questions. It allows for the rapid delivery of insights and reports that can drive competitive market advantage.
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Data lakes or Data Warehouses: Conclusion
Data lakes and data warehouses are not mutually exclusive, despite their differences. Often, the same organization may need to implement both solutions simultaneously but for different reasons.
However, using both a data warehouse and a lack of data in the same company can result in unnecessary costs, security issues, or duplications of data. A couple of years back, Data Lake House came out. This new data architecture provides both structured data and lower storage costs.
A data lake house is a single repository that allows the storage capacity of any type of data, management of several data pipelines, and application schemas to large datasets. The above issues are also not possible with this solution, as it does not involve managing multiple solutions simultaneously.
Consider your own use cases when deciding the data management solution you should adopt for your business. You may find that a data lake is the best option if you want to store both structured and unstructured data.Your data scientists and machine learning specialists will need raw data to do their work.
A data warehouse is a better option if your data is complex and highly structured, but you want to give many users in your organization access to it quickly so they can build reports and use BI.
Large enterprises that have a lot of data coming from different sources may use both solutions, or they might look at implementing the leading-edge lake house architecture. You will be able to find a solution that suits your business needs.
