What is data lake architecture?
Data lakes, the storage repositories, are one of the modern trends of holding enormous volumes of raw data in their native format. Read more below about data lake architecture?
This data, including the structured, semi-structured, and unstructured data, don’t have any need to be defined until the need for data arises and the data is finally channeled to the right mediums. These data lakes remove the complexities of storing your data to make it faster for running with interactive analysis and streaming.
Data Lakes democratize the data within your enterprises. The fact that it’s budget-friendly and pretty cost-effective makes the process of storing data within pretty convenient. This allows the analysts to focus on finding meaningful patterns in the data registry.
Quite unlike a hierarchical Dataware house, data lakes consist of a flat architecture. Elements in a data lake architecture are given unique identifiers along with tagging them with a set of metadata information.
Because of the growing varieties and volume of data in enterprises now, data lakes are a reasonable approach. This is a growing trend since IoT and cloud-based applications have become common for big data.
But most importantly, they play a major role in some of the common factors of our technological sectors.
Data Lake Architecture
A business data lake usually consists of lower levels that represent data that are not put to use often. However, the upper levels show real-time transactional data.?
The same data flows the system without any latency. Some of the important tiers in Data Lake architecture are:
Ingestion Tier – These tiers, being on the left side, depict a variety of data sources that could be loaded into the data lake in batches.
Insights Tier – These tiers, being on the right, represent the research side. Here, insights from systems are gathered and used and frameworks like SQL are used for data analysis.
Security – Data Lakes are usually allowed very restricted access since raw data from enterprises is pooled here. However, it’s still important to think of this aspect though.
Metadata – Some of the common insights about your data, how it performs along with tags about description, everything is considered in metadata. It also consists of descriptions of how the data needs to be used and what is the purpose of this data.
Governance – Governing involves monitoring the operations engaged with data lakes as it becomes vital at some point to measure the performance and adjust the data lakes.
Archive – Data Lakes are bound to run into performance issues unless you have an additional relational DWH solution. While this may not be necesary, it still helps keep some archive data within the data lakes.
Offload – Again with the DWH solutions, you can offload time consuming ETL processes to your Data lakes. The ELT paradigm of data processing always puts the data transformation step at the end. This extraction from the source systems and loads into the database.
On the other hand, the old ETL of RBAR (Row-By-Agonizing-Row) is in direct contrast with the set-based processing that’s performed by Relational databases and set-based processing that formed the whole basis of SQL.
When it comes to ELT, we extract the data from the source databases and put the same in the data lakes. The SQL transformations are done in the Cloud Data Warehouses and the whole data is loaded to the target tables.
This is a cheaper/faster solution for enterprises with less time to spare.
Master Data – Master data comprises of such data that needs to be ready for use whenever needed. For serving master data, you either need to store your MD data on the data lake architecture, or reference it during the execution of ELT processes.
HDFS – Being a budget-friendly solution for all the data types, it happens to be a landing zone for data that are not currently used in your systems.
Distillation tier – It takes the data from the data registry and converts the same into structured data for ease with analysis.
Data lake architecture processing tier– It runs analytical algorithms with varying real-time batches to generate structured data, again for easier analysis.
Over to data lake architecture
A proper data lake has the ability to remove silos or open up mining of results.?
It happens to be one of the most essential elements that are required for harvesting big data as a core assets.
These data lake architecture assist in extracting insights from data repositories along with enhancing the overall decision-making process for enterprises.