Choosing the right technology for Data Lake Implementation
Efficient data management is key to success for organizations today! It is beneficial not only for effective decision making but also for various business processes like personalization, asset management, IoT data monitoring, and much more.
A data lake can be defined as a storage repository having the ability to store voluminous amounts of data, be structured, unstructured, or semi-structured in its raw form. They are best for storing streaming data.
Choosing data lake technology for your organization big data is very exciting! One must decide data lake architecture and the required technology stack to implement it.
A data lake framework may consist of different zones:
- A landing zone /transient zone
- A staging zone
- An analytics sandbox
From the above zones, the staging zone is mandatory and rest are optional.
Landing zone:
In this zone, data pilot cleaning and /or filtration of data takes place where a processing engine embedded in the landing zone performs this functionality.
Staging zone:
Data in this zone appears in two ways. One, from the landing zone, and two, from internal or external data sources. Data that does not require preprocessing appears in the staging zone.
Analytics sandbox
Data analysts reserve this zone for data experiments. Here, the results of data experiments are not directly incurred by the business. Algorithms are applied by analysts to raw data and they may get no significant outputs.
In addition to the above-mentioned zones, a curated zone may exist in some sources. This zone contains organized data prepared for data analysis. Data scientists differ in their opinions about whether the curated zone can be considered as a part of the Data Lake or not. The curated data zone is alike the traditional data warehouses in many ways. The difference between the two is, curated data zone contains both traditional and big data. So, it is referred to a big data warehouse.
Also Read: Data Lake Essentials – Part 1 – Storage and Data Processing
Technology alternatives for data lake implementation
Data lake implementation may face roadblocks in the form of some infrastructural and process decisions. After learning about different data zones, the primary step is to decide the right technology. Below is a list of defining factors that decides the right technology stack for implementing a data lake framework:
- Scalability
- Storage and processing of data
- Needed architecture of a data lake
- Physical storage options – In-cloud or on-premises solutions
- Data lake integration with existing IT architecture
You can decide right one from the myriad of names available like MongoDB, Apache Cassandra, Hadoop Distributed File System, Amazon S3, and many more.
Following are some evaluating criteria to decide the right technology:
- Security and access control
- Data ingestion
- Metadata management
- Managing and monitoring
- Data governance
Data Lake as a service
Some leading platforms like Amazon Web Services, MS Azure offers data lake service. It is a paid service. In return, you get a predefined set of technologies installed in the cloud and get rid of a frequent maintenance issues. They offer a wide range of functions like storage, processing, streaming, and analytics.
A data lake technology is indeed tangled magic. It overcomes obstacles of traditional data houses and nurtures a culture of data-driven decision making. Decide the right technology for data lake implementation depending upon your business needs and be ready to swim like a pro in the data lake.
