The security of your company’s Data Lake depends on the setup and management of the Data Lake and the security practices to which your company adheres. Much like anything technological, the system itself contains inherent safeguards. Still, users can take actions to make the data lake even more secure or help it devolve into a data swamp.
In this article, you will learn how to keep your Data Lake security clean of common sludge that clogs up the digital pipes while also coming to understand the importance of a data lake and the security that SatoriCyber provides.
Here is the breakdown of how this guide will walk you through Data Lake Security:
- What is a Data Lake?
- Data Lake vs. Data Lake House
- Data Lake vs. Data Warehouse
- Data Lake Security vs. Security Data Lake
- Data Lake Security Best Practices
- Conclusion
This is part of our comprehensive data security guide.
So, what are you waiting for? Let’s dive in!
What is a Data Lake?
A Data Lake is a repository or system of data stored in its raw format. Having access to data’s natural or raw format means it is as fresh and pure as possible. This data has never gotten manipulated, making it ideal for reporting, visualization, advanced analytics, and machine learning.
Some of the most common forms of data stored in a data lake include:
- Binary data
- Data from emails, documents, or PDFs
- IoT data
- Sensor data
- Social data
- Structured data from databases
- System data
However, many different data wells can culminate to create a useful Data Lake for your business.
Why Should You Use a Data Lake?
The design of the Data Lake was to help companies create and maintain access to a scalable, low-cost repository of reliable, raw data, which they could draw from to create actionable reports, visuals, and projections.
Moreover, the Data Lake is convenient for businesses of all sizes and capabilities. It can get created on-site, utilizing an organization’s data centers or a cloud-based entity.
Criticism of a Data Lake
The most common criticism of a Data Lake is simply a dumping ground for data. Criticizers think of it as a data swamp, graveyard or back room that you never use because there’s too much stuff packed inside even to attempt cleaning it out.
However, tif you maintain your Data Lake and keep it secure and well governed, this perception of a dumping ground never comes to fruition.
Data Lake vs. Data Lake House
When Mr. James Dixon, then chief technology officer at Pentaho, named these terms over a decade ago, he just wanted to go on a vacation.
Nevertheless, the Data Lake and the Data Lake House concepts help understand what each term means concerning one another.
Data Lake | Data Lake House |
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Data Lake House Explained
Ultimately, a data lake house is a more organized version of the Data Lake, combined with a Data Warehouse. A Data Lake House offers easier data ingestion while maintaining cost-effectiveness.
Occupying a Data Lake House also allows more people (with authorization) to use the data for the company’s betterment.
A Data Lake House takes the best qualities from a Data Lake and a Data Warehouse and combines them.
Data Lake vs. Data Warehouse
Data Lake | Data Warehouse |
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Data Warehouse Explained
Before the Data Lake House was a widely viable means of processing Big Data, the Data Warehouse helped business people manage the massive amounts of data swimming around in their Data Lake.
Data Lake Houses get preferred due to their cohesive and inclusive nature, but implementing a Data Warehouse isn’t completely outdated.
Data Lake Security vs. Security Data Lake
Data Lake Security | Security Data Lake |
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Data Lake Security Best Practices
Any Data Lake not destined to become a Data Swamp gets endowed with its security procedures. Likely these procedures are put in place by the data scientists or the cloud-based entity that helped create the Data Lake.
Yet, there is always more you can do to keep yourself and your company safe regarding security. So, here are the best Data Lake security practices that should be a staple throughout your company.
Ensure Data Access is Audited and Logged
There is no excuse for anyone (including administrators) to access the company’s Data Lake without leaving an audit trail behind. When you have a log, you can get to the root of any issues or questions without devising who was accessing the Data Lake at any particular time.
Moreover, you should conduct a thorough audit of your Data Lake according to security and compliance requirements. Timely audits will help you and your company stay ahead of any potential security threats lurking on the banks of your Data Lake.
Always Know Where You Put Your Sensitive Data
While this security tip might seem obvious, dealing with a high volume of Big Data, even your most sensitive data can slip through the digital cracks. Therefore, you must know where your sensitive data assets are on an ongoing basis.
Even if that is the only thing you know about the Data Lake and let your IT staff handle the rest, you need to know where you put your sensitive data. Because data keeps on moving and changing, this can’t be a once-a-quarter periodic scan, but has to be continuous.
Only Store What You Need
Minimalism in data storage is just as important as minimalism throughout your surroundings. Even though Data Lakes can store massive amounts of information, that term can become relative when dealing with Big Data collected from a company.
So, while using a Data Lake to store information is a workable solution, you should only keep the data you need.
The more data you store, the more sluggish your system becomes. You don’t want to bog your Data Lake down with unnecessary information, less it will cause problems for you and your company in the future.
A good time to determine the data that you need is through audits. If your company is going another direction or otherwise finds the information useless, don’t be afraid to let it go to make room for important information to come.
Encrypt Data at Rest, and In-Transit
Most people dealing with sending and storing data understand the importance of encrypting data in transit. However, it is equally important to encrypt your data while at rest.
Since your data spends most of its time at rest, the likelihood of it getting attacked while not in transit is a viable possibility. So, to keep your data safe, it’s always a good idea to keep it encrypted both at rest and in transit.
Continuously Review Your Network Configuration
Your network configuration is a great indicator of something amiss in your Data Lake security. So, just like you audit your Data Lake, you want to review your network configuration continuously. If you or your IT staff notices anything out of the ordinary, it should be dealt with immediately.
Maintain Clear Security Policies
Many breakdowns in security protocol are not malicious on the part of the employee. Most times, security breaches are due to negligence.
Therefore, maintaining clear security policies is vital to keeping yourself, your employees, and your customers safe.
You know, as a business owner, as much as you might like to, you’re not going to do everything yourself. You need to delegate your tasks. So, you want to find people who have a talent for the work you need to delegate.
However, having a talented staff is not enough. Ensuring safety means that every employee must follow security procedures regardless of their IT capabilities. So, it is up to you to maintain clear security policies followed by everyone, including yourself.
Conclusion
In summation, there are many ways to help bolster your Data Lake security. While not every threat is avoidable, you can avoid most security breaches by maintaining a clear, present, and unchanging Data Lake security protocol. Do not take chances when it comes to your Data Lake security.
Threats are everywhere, so you always need to be prepared. Stay active, alert, and aware of giving yourself the best opportunity to avoid a security disaster.
Satori For Data Lake Security
Satori, The DataSecOps platform, gives companies the ability to enforce security policies from a single location, across all databases, data warehouses and data lakes. Such security policies can be data masking, data localization, row-level security and more.
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