Data Lake Security - Satori
Data Lake Security
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
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, if 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
| Data Lake | Data Lake House |
|---|---|
| - Stores raw data. - Accommodates large amounts of data. - Provides direct access to source data. - Mass of possibilities in one large pool (or lake). |
- Schema support for both writing and reading. - Offers mechanisms for data governance. - Separates storage. - Standardizes storage formats. - Support for structured and semi-structured data types. |
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.
Data Lake vs. Data Warehouse
| Data Lake | Data Warehouse |
|---|---|
| - Undefined data resides here. - It puts data in a holding pattern. - Highly accessible. |
- Central repository for structured data. - Easier to understand/find data. - More user-friendly for schema reading. - More expensive to add data to (schema writing). |
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 Security vs. Security Data Lake
| Data Lake Security | Security Data Lake |
|---|---|
| - Keeps data in the data lake secure. - The security efforts keep your Data Lake safe. - Can be added to or improved upon. - It is a joint effort throughout the company. |
- Type of Data Lake where security events and alerts are sent. - Optimal holding place for security-related events to get reviewed, analyzed, and investigated. - Only accessible by select, trusted people within the company. |
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.
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
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. Threats are everywhere, so you always need to be prepared.