What is a Data Lake?

A Data Lake is used to refer to a massive amount of data stored in a structured, unstructured, semi-structured, or raw form.

The purpose is just to consolidate data into one destination and make it usable for data science and analytics algorithms. This data is used for observational, computational, and scientific purposes. The database has made it easier for AI models to gather data from various resources and implement a flawless system that can make informed decisions.

The Evolution of Data Lakes-A Brief History

Before the introduction of Data Lakes, Data Marts, and Warehouses were used. They were centralized storage units for structured data and usually had access limited to only one department.

Both these concepts were around since the 1980s and were proving to be insufficient with the growth in technology. Data Scientists had issues extracting information from these storages. In 2010, the CEO of Pentaho introduced the concept of a data lake where the data would be stored in its raw form.

The concept of Data Lakes came into being around 2011 when companies started struggling with Data Silos.

Hitachi (formerly known as Pentaho) was the first data supplier to start exploring the problems at hand and devised Data Lakes. Companies started working on this process of having an unstructured pool of data.

It was Yahoo! that set up a team of data engineers to work on software that would support Data Lakes. Yahoo! introduced Hadoop which grew to become a crucial tool globally used by all top-performing companies including Twitter, Uber, Netflix, and many more. Microsoft’s Azure offers Azure Data Lake, one of the most widely used data sources platforms, and a widely accepted data center. Google Cloud, and AWS (Amazon Web Services) are also major data centers.

Deep Dive of Data Lake Architecture

Data Lake Architecture is the designing and planning of a system where data is securely stored and easily accessible by analysts. They do not need data to be secured in an organized form, but they do need to consider numerous steps throughout the design process.

Key Components

The important key factors of any Data Lake to implement a successful architecture are:

· Security: Keeping data secure and safe from threats is a crucial step while designing the architecture. The designers take security as a priority to keep information protected.

· Governance: It is important to have full knowledge of the data and the operations that can be performed and updated as per requirements.

· Metadata: The Lake needs to be interlinked with other resources to ensure a better user experience.

· Stewardship: The DL needs to have proper stewards defined at the time of designing. The steward can either be a specialist or the lake owner themselves.

· ELT: The Lakes operate on the Export, Load, and Transform policy only to keep the data hybrid.


The architecture of Data Lake is divided into five sub-layers each working on its own principle.

· Ingestion Layer

The ingestion layer, as its name suggests, is there to “ingest” the data into the lake. The ingestion layer extracts data from sources and providers across the web and incorporates them into the data storage. A benefit of DL is that data ingestion can be in any file format. After ingestion, it is organized into the lake in relevant folders.

· Distillation Layer

After Ingestion, the Distillation Layer transforms the data into a structure. This structured data is then organized into relevant files and tables. This data processing makes the data easily accessible for data analytics and business intelligence purposes as queries are performed on it.

· Processing Layer

The processing layer takes care of all the queries performed on the structured data. Data Lake allows users to carry out queries in batches, separately on each folder, or even in real-time.

· Insights Layer

It is also known as the output layer. It is the layer that takes care of queries executed by the data analyst. The output from the execution is also displayed inside this layer. The output received is in tabular form or arranged in dashboards which makes it easier to draw “insights” from it. The data analyst may use a DBMS or python to execute the queries.

· Unified Operations Lake

This layer is basically the management department of the lake and supervises the operations of each layer.

Explaining Data Lake File Formats

Data Lakes take data from numerous sources and organize them into files. The current format being used in the market is the CSV which is column-oriented. The file formats are used to make the storage and sharing of data across networks easier.

The main tools used are Apache Parquet, Avro, and Arrow where each one of these has a specific usage in the lake. Parquet is more speed efficient while Avro has a better schema description language.

Explaining Data Lake Table Formats

Data Lakes table formats are designed to store Big Data. The conceptual basis is on the principle of abstraction as the complex details are hidden and the data is presented in an easy-to-read format.

The tables store the data in a tabular form and make it column-oriented. It is then easier to execute any queries on the data. Data tables make life easier for data analysts to extract and alter information in the data logs. However, it also comes with drawbacks as a few functions cannot be performed.

The following is a list of functions that can be performed on the data and the reason they are important for data manipulation:

· SQL Support: Perform INSERT, CREATE, ADD, and MODIFY functions.

· Schema Evolution: Changing the data files by modifying column names or even adding a new column. The table format implements the change across all tables.

· Acid Transactions: This feature ensures that all changes are successfully implemented, and there is no inconsistency or non-concurrency in the data.

· Time Travel: This feature makes it possible for users to go back into the history of the data and reverse edits. This makes it easier to perform audits, and recover accidentally deleted data. The time travel feature also allows doing more than one query at different time locations of the data.

Challenges of Adopting a Data Lake Architecture

The Data Lake technology is fairly new and has several issues. The following are the challenges creators and users may face:

· Difficulty in identifying the use case.

· Requires funds and implementation cases to get investors onboard.

· Does not work efficiently for smaller data sets.

· The open-source nature gets confusing as everyone implements their own system.

Benefits of Adopting a Data Lake Architecture

Data Lakes offer a brilliant solution to companies dealing with Big Data. The following are some of the Key Benefits of Adopting a Data Lake Architecture.

· Data Silos

Data Silos refer to the limitation of access to data to specific departments and organizations. DL got rid of this concept by introducing an open-source system. Any user can access data and use it for analytical and innovative purposes. The data is consolidated into a single location which also reduces the duplication of data across multiple locations.

· Hybrid System

There is no restriction one file extensions while uploading data. Users can upload structured, semi-structured, and unstructured data. They can upload multimedia files, pdf documents and excel files as well.

· Pre-defined Schema

Data lakes can have totally unstructured data on them. The users do not have to follow a predefined set of instructions to execute queries. They can use their own systems to do all of this. They provide cost-efficient cloud-based storage that has the features to perform complex analytic procedures.

This article further explores the benefits of integrating Data Lakes into your Business.

What Questions Should You Ask Before Adopting a Data Lake Architecture for Your Company?

The most important thing to know before adopting one is what data do you need to target. Companies often go in blindsided by what they are looking for. With this knowledge, they can approach relevant databases only.

What function do you need to perform for data analysis? It is really important for companies to know all the things they need to extract from the data. This would also include them knowing the skill and tools required to convert this data into a structure and then perform the queries. Knowing this would also help them know whether a data lake; with mostly unstructured data, or a data warehouse; where all the data will already be structured, is more suitable to their needs.

Is Data Lake House Different Than a Data Lake?

Data Lake House combines the features of both Data Warehouses and Data Lakes and implements them on a single system. Data Lake Houses are hence the best of both worlds. The basic features of a Data Lake House are mentioned below:

· Concurrent Functionality

· Provides access to data files and tables

· Allows Structured and Unstructured data types.

· Supports all schematics

The following are the advantages of a Data Lake House over Data Lakes and Data Warehousing:

· Time and workload efficient

· Cost-effective

· Simplified schema implementation

· Reduces Data Duplication

Conclusion Section

Data Lake House has introduced an innovative methodology for Data Base Management. It has made it easier to consolidate data to a single access point. The DL is of utmost importance for Machine Learning as a broader range of data can be accessed and visualization of AI models is improved. The question arises of where to find a data warehousing tool that is easy to use and has an impressive data catalog. Don’t you worry we have a one-worded wonder house for you! Integrate.io.

Integrate.io provides a single-point solution to all Big Data storage and processing issues. Integrate.io offers users integration, processing, and analytical benefits to its users where analysts can perform predictive analytics. Integrate.io is currently offering the following products and services: ELT, ETL, data warehousing, and API generation.