Data Lakes are a method of storing data within a system from physical repositories such as cards, keyboard inputs and tapes. The formation of data lakes idea is to have a single storage of all data of a particular enterprise ranging from raw data to transformed data. The data lakes mainly include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and even binary data (images, audio, video). Thus data like is creating a centralized data store accommodation for all forms of data.
Actually databases are a limited tool because they require specific ‘contexts’. For eg., The database for Accounting Data must be a distinct entity than the database for Logistics Data, although they may need to connect and have mutual referencing structures. So, most of the data structures are still under construction to improve their unique structures.
Every database contains larger clusters of similar data, which are the Data Warehouses, but the process may slightly differ. Data are grouped in the same manner while pertaining a common context.
Main advantages of Data Lakes comparing to Data Warehouses are given below:
- Data Warehouses store only structured processed data, while Data Lakes also store semi-structured as well as completely raw data.
- The Data Warehouse design implies expensive large data volumes whereas Data Lakes are specifically designed for low-cost.
- Data Warehouses can be used by a pre-defined structure of data storage only, while Data Lakes have great flexibility towards dynamic structural configuration.
- Data Warehouses can store data which are fully matured and has a clearly defined context, but Data Lakes can store data which are still maturing.
- Moreover Data Lakes have the ability to derive value from unlimited types of data which Data Warehouse hasn’t.
From the above points we can realize that without Data Lakes the Artificial Intelligence would be heavy, slow or even impossible to implement new technologies in a cost-effective manner.
Data Lakes work similar as an actual lake, into which data flows from several streams such as M2M, log files, real-time data collection, CTI etc and then we are able to run appropriated analytics towards it, therefore extracting added value information. Accessing data from different sources like SAP or DB2 requires both specific tools as well as licensing which represents time and money.
To extract meaningful information out of those distinct IT environment represents creating a detailed map of what data is relevant towards which requirements and then developing a suitable code that can gather such data in the appropriate sequence to produce information that shows new meaningful data.
How to get the advantages of Data Lakes in a business?
- First determine your Business Objectives.
- Collect the data that will enable you to reach your Business Objectives.
- Identify what goal you want to achieve using data lakes.
- Targeting the right prospects.
- Increase Sales and Revenue.
A Data Lake Architecture has the following layers:
Data Source Layer
This layer provides data streams from corporate systems or other sources of both raw or structured data via suitable plugins like API in origin storage format.
Processing and Storage Layer
The first processing layer provides security towards the data streams by giving visibility classification and control like authorizations and permissions. Second layer gives multi-level stratification like clustering, data sets and attributes. Third layer gives labeling towards highly sensitive classes of data and the fourth layer gives authorization.
After collecting the valuable information by processing layer over the Data Lake content, it must be either integrated back on Corporate Systems and made available for several types of user profiles, who will use it to leverage and gain efficiencies such as the role of Integration and Governance.
User Interfacing Layer
Different user profiles must visualize data and information in different manners while other visualize more detailed and exposing details and correlations.
Points to remember while implementing a Data Lake:
- Construct a best suitable Architecture which addresses available data.
- The Data Lake should be created according to the available Data Sources and Type and not according to what is required.
- The design of Data Lake must be guided by which type of data you are able to dispose during processing and not what will be useful.
- The processes such as Discovery & Ingestion, Storage & Administration, Quality & Transformation and Visualization & Interaction must be addresses separately.
- The native data types should be focused.
- Always assure that the Data Lake structure and processing are capable of fast Data Ingestion independently of its source, not to create a bottleneck at the very beginning of the entire process.
- Data profiling, security and policies must be defined in advance as well as tagging, correlation rules and workflows.
- We should develop proper strong and agile querying processes or the Data Lake will not be responsive as per your time to market requirements.
- Proper algorithms and codding that allows swift discovery of correlations and unification criteria must be clearly defined in advance.
- Like this, a proper Metadata Catalogue also needs to be defined in advance and the process must be fully automated in order not to cause errors or processing blockages.