Given the sudden explosion of raw unstructured and semi-structured data over the last decade or so it was prudent that there has to be a separate storage reservoir and mechanism to store and retrieve useful information from this data. A data lake provides a very robust and versatile architecture at an enterprise level to access and manipulate unstructured/semi-structured data. The core to the success of any data lake implementation is the ability to articulate the value of such implementation to the stakeholders and convince them to join this journey.
A typical data lake setup on AWS:
In this article, we look into a few best practices when dealing with Data Lake.
Ease of navigation
Data lake consists of raw unstructured and semi-structured data from different sources. It is very important that this data be streamlined and stored in a very structured manner.
The end-user should be able to navigate and locate his data of interest with relative ease. To achieve this a metadata and underlying mechanism are provided by almost all of the data cloud providers.
The data stored on a data lake should always be accessible to any user and should not require technical intricacies. Hence it is very important to simplify the data storing mechanism on a data lake that encourages the adoption of data lake by the business users which would help them gain insights from this data to drive strategic business decisions.
The users should be able to locate their data on a data lake with relative ease. Business users have a varied level of data analytic tools at their disposal. The end-users should be able to easily ingest the data from the data lake into any data analytical tool of their choice.
Data theft is one of the biggest concerns for all organizations and is also one of the most underestimated threats. Over the years the sophistication of such threats has increased. So it is very important for an organization to have a robust security model for their data lake implementations so that the overall risks can be mitigated.
The most key aspect when it comes to data lake security is to define multiple zones that give the flexibility to structure logical and physical isolation of data. Typically it is recommended to use 3 to 4 zones. This setup can be adapted based on size and business use.
With the advent of different data governance compliances, it has been more than crucial to devote resources and time to implement those sophisticated data governances. Data governance policies should be clearly defined and established at an enterprise level. In line with such stringent data governance policies organizations are not allowed to load the data lakes with more and more user data for searching trends for actionable patterns to help drive strategic business decisions.
Establishing clear policies revolving around data compliances for adding data to the data lake is of paramount importance.
Every traditional and nontraditional data storage and retrieval mechanism needs to be highly scalable and data lakes are not an exception. Given the inconsistent format and schemas of nonstructured and semi-structured data, the data lake must be able to scale and provide optimal performance to the end-user.
For a data lake to be scalable some questions need to be addressed:
- Would a local, cloud, or hybrid setup help my data lake to scale?
- Will vertical or horizontal scalability would provide peak performance?
- What limitation of my data lake do I need to address first?
- What usage patterns would help me design my data lake that can scale?
The need for spending additional time and resources for implementing data lakes is often questioned today as organizations have spent a considerable number of resources and time in designing and building data warehouses. Due to the phenomenal increase in the amount of nonstructured and semi-structured data coming in from different data sources its time organizations embrace data lakes that can complement the flexibility and robustness of a data warehouse.
In this article, we tried to look into the industrial best practices that need consideration while designing a data lake. Covering all of the above key functions it’s possible to significantly simplify the entire data lake design experience.