Companies that learn from data are more responsive to market needs and organizational goals. It all starts with storing the data. There are various options for this, such as a data lake or a data warehouse.

Data lakes and data warehouses are both used to store data. Companies can perfectly combine a data lake with a data warehouse. The two forms are used for other data applications.

The main difference is in the type of data that the repository contains. A data lake contains raw data that does not yet have a final destination or use. A data warehouse, on the other hand, contains structured and filtered data where the business already knows what the purpose of the data is.

There are also two other differences to note. While a data lake is intended more for researchers and analysts, anyone in an organization can start working with data from a data warehouse right away. The structured data is actually cast in graphs, tables and charts.

Finally, the accessibility of data is different. A data lake offers the most benefits here, as it is easily accessible to make adjustments quickly. An adjustment in a structured data warehouse is rather more complex. We’ll go further into the differences between a data lake and a data warehouse and what exactly they offer businesses.

Data Lake: the smart worker

Raw data is first collected in a data lake. This data has therefore not yet been incorporated into a timetable. Therefore, files that are hidden in the datastore but never used do not take processing time. The specific principle on which the storage system works is Schema on Read mentioned: only when the data is read is it necessary to put data into a schema.

This system makes a data lake intelligent. Data lakes are very useful for companies that want to make predictions based on the data. People who have the necessary knowledge or tools can analyze and prepare data faster and more accurately.

Data lakes are very useful for companies that want to make predictions based on the data.

Merging all the raw data requires huge storage capacity and processing time. For a data lake, there are players in the market who translate, manage, and monitor the unstructured data. Some of the vendors offering solutions in this space are Azure Data Lake Storage Gen2, AWS Lake Formation, Google Big Lake, Qubole, Cloudera, and Snowflake.

In business applications, a data lake is useful for machine learning because the data is flexible and can be quickly analyzed. In IoT applications, for example, huge amounts of sensor data can be processed at incredible speeds. Retail is able to deliver an omnichannel experience using a wealth of data collected about the user.

Data warehouse: the hard worker

Data coming from different departments of a company, from systems of other companies and from user applications are brought together in a data warehouse. This information is immediately formatted and processed to match data already in the warehouse. That’s what they call it in technical jargon Schema on Write. For instant processing, a data warehouse is given the title of hard worker.

Because data in a data warehouse is instantly accessible, it can help companies make decisions. It visualizes company information and is particularly useful in the financial world, where companies receive enormous amounts of data.

Because data in a data warehouse is instantly accessible, it can help companies make decisions.

In addition to cloud storage, on-premises servers are also possible for a data warehouse. In general, it’s best to aim for a server with at least an 8-core CPU, at least 32GB of RAM, and 200GB of free disk space. The software is recommended to run Red Hat Enterprise Linux 7.3 or later (older versions are no longer supported), CentOS 7.2 or later, or Oracle Enterprise Linux 7.5 or later.

Avoid a data swamp

In order to get the most out of a data lake and data warehouse, it is necessary to work on data quality and maintain the data. Otherwise, you run the risk of your memory becoming one data swamp, a disorganized dissemination of data. You can avoid this with the following strategies:

• Collect only useful data
• Determine a strategy in which you set a clear goal for the data
• Ensure employees always have access to data relevant to their position and department
• Combine the right data

Investing in data storage and developing a data approach can be time-consuming and expensive in the early stages, but pays off in the long run. They improve the quality of the data so that you can gain more and more useful insights from the data. This also makes it easier to respond to existing needs, which makes a company more attractive to customers and partners. It also saves time because the process of decision-making and decision-making is accelerated.

Tips for efficient data storage

Setting up a data repository can be a time-consuming task. It is therefore important that the storage can be used efficiently and safely in the long term. Consider the following points:

• Enable data management

With data management you ensure that data actually does what it was originally intended to do. In addition, it improves the quality and security of the data. In order for data management to work efficiently, standards, processes and metrics must be set up right from the start. This can be done, for example, by setting a maximum file size so that the data remains clear.

• Create a data catalog

Having an overview of the data your data lake or data warehouse stores simplifies working with data. For example, you can include in the catalog what connectors are needed to work with the data, where and how long data is stored, and what application uses the data. These questions can be customized to meet the needs of your organization.

• Activate search

In addition to a data catalogue, it is important that the right people can search the data to get started quickly. For example, you can search by size, date, and content.

• Make it safe

Sensitive information is not something that cybercriminals can easily fish for. Make sure your data is stored securely. Limit access to data sources to those employees who actually need the data, and ensure their access is through a secure channel. Create sufficient and regular backups of your data to be able to restore them quickly in the event of data loss and do not keep databases longer than necessary.

This article originally appeared on September 16, 2021. Last updated on 03/03/2023 by Jens Jonkers.