Series 1: Introduction
Best Practices To Consider For Building Scalable Data Lakes on Cloud
Data lakes have presented additional transformational capacities and new possibilities for businesses to represent their data in a distinctive consumable and uniform manner.
But with the rising popularity of Data Lakes, the risks of them transforming into silos and swamps have also increased substantially.
The way a Data Lake is built and managed plays a crucial role in the amount of value it delivers to an enterprise. Here are some of the best practices one must consider when building Data Lakes:
Allow relevant data as per the business problem: Before anything, one must think about why a data lake should be built and what business problem it is aimed at solving.
Maintaining a clear goal in mind on why a data lake is required aids people to stay focused and gets the job done efficiently and easily. The core basis of a data lake must be clear, and should be implemented for all the needed use cases.
Even a well-organized data lake may end up as a data swamp if a company does not set parameters on the type of data they desire to gather and why.
Facilitating correct metadata for search: It is crucial for each and every bit of data to have information related to it asmetadata in a data lake.
Creation of metadata is quite common among modern businesses as a way to properly organize their data, and prevent a data lake from transforming into a data swamp. This acts as a tagging system that allows people to easily search for various types of data.
In the case where there is no metadata, the people accessing the data may end up in complex situations where they don’t have a proper way to search for information.
Having a grasp on data governance: Data Lakes must define clearly the manner data should be treated, handled, retained, etc. Systematic data governance equips enterprises to maintain a good level of data quality throughout their whole data lifecycle.
The absence of rules when it comes to governing how data is handled leads to it getting dumped into a single place without any thought about how long it is needed .
It is crucial to have assigned roles to provide designated individuals the access to and responsibility for data. Prioritizing data governance as soon as firms start collecting data is vital, to see to it that systematic management principles are applied to it.
Maintaining a data cleaning strategy: A data lake may unintentionally end up as a data swamp, unless enterprises have a proper plan in place for cleaning their data.
In case the data has any errors and redundancies, it shall be of no use to anyone. The data shall lose its accountability and cause firms to reach incorrect conclusions.
Moreover, it might take months or even years for someone to realize that they have inaccurate data. Businesses must be proactive about deciding what specific things they should regularly do in order to keep their data lake clean.
Design Practices when Building Data Lakes
- Design of the various modules while building data lakes like Ingestion and Processing
- Data movement and should be based on industry standard data processing architectures, such as Lambda Architecture.
- Proper logical zoning of data such as:
Landing Zone
An immutable zone to store data as-is with no transformations
Centralized Zone:
A zone with data laid out as proper queryable format, with data cleansed
Business Zone (Platinum Tier):
A zone to have consumption ready data for reporting
- Data sources for the architecture are the source of truth of data – extract data from source with no alterations.
- Prioritize the usage of Azure PaaS services as part of the cloud data architecture.
- Initial and incremental data load consideration for the identified sources.
- Ensure security and access controls using the principle of “least privilege” (PoLP).
- Decisions on scaling are driven by current information about the expected growth of data from the underlying sources.
Data Privacy
Protect personal data against unauthorized processing and against accidental loss, or damage, using appropriate technical or organizational measures
Data Governance
Protect personal data against unauthorized processing and against accidental loss, or damage, using appropriate technical or organizational measures
By following the proper practices and guidelines, enterprises can make effective use of data lakes for the progress and productivity of their business.