A data lake can be defined as a storage repository having the ability to store huge amounts of data, be structured, unstructured, or semi-structured in its native form.
The need for a data lake
Currently, organization data are stored in isolated data silos forms across different storage systems like data warehouses and databases. A well-built data lake architecture enables you to:
- Power data science and machine learning
- Centralize, consolidate and catalog your data.
- Quickly and seamlessly integrate diverse data sources and formats
- Democratize your data by providing users self-service tools.
When compared to a traditional data warehouse, Data Lake has much more in its bag.
Data Lake can scale to hold any amount of data at low cost, regardless of type. Whereas, data warehouses can store only structured data and are expensive due to vendor costs. Data Lake can offer versatile features like flexibility, scalability, and facility of storing raw data that empowers data analytics and data scientists.
Data Lake Architecture Structure
In a data lake architecture, content repositories play the role of interface between the source of data and the target data repository (Data Lake).
The data lake architecture diagram shows the loader, connector, and crawler that connects with the different interfaces in the data lake. Physical storage plays a vital role in the foundation of any lake architecture. Following are some principles and requirements to be considered while evaluating cloud-based data lakes storage technologies:
- Exceptional scalability
- High durability
- Independence from a fixed schema
- Separation from computing resources
- Support for all types of data.
In the data lake architecture diagram, we can see additional layers on the top of the core storage. These layers are for the retention of immutable raw data. The additional layers usually have some added structures for assisting in-effective data consumption like reporting and analytics.
Also read about: Data Lake Essentials – Data Catalog and Data Mining
Choose File Format
On contrary to traditional RDBMS, the architecture of Data Lake provides an extraordinary amount of control over the storage of data. All parameters such as file size, type of storage, degree of compression, block sizes can be controlled. There are some Hadoop-oriented ecosystem tools used commonly in data lake architecture.
- File size
- Apache ORC
- Same data, multiple formats.
Like all other cloud-based deployments, security has been topmost priority for data lake architecture. It must be embedded from the beginning so that complete security can be ensured. Data lake architecture guide reveals three primary domains of security:
- Network Level Security
- Access Control
Access and Mine the data lake
In contrast to traditional RDBMS’s method of ‘Schema on Write’, Data Lake is built on ‘Schema to Read’ technique. It prevents the primary data store from being locked into a fixed schema. Raw or mildly processed formatted data can help data analysis tools to make it appropriate to the analysis context. These are beneficial for various purposes such as:
- Data Processing
- Data warehousing
- Interactive Query and Reporting
- Data Exploration and Machine Learning
A well-built data lake architecture contributes to harvesting enterprise big data as a core asset. Go for Data Lake architecture and enjoy the exceptional features of data storage.