Data Lakes : An overview
The relevance of data lake
- Data analysts can gain more insights from the central data store by using SQL
- Availability of the complete dataset can improve the accuracy of machine learning models by leaps and bounds.
- Visual dashboards and reporting tools can be developed faster and more easily with the central database.
What’s more, people with different skill-sets and tools can perform all these tasks simultaneously on the data lake without moving the data elsewhere, even as more data is streaming in.
Advantages of data lakes
Flexibility at low cost
Data lakes utilize open formats to process all sorts of data and make it available for analysis and machine learning at a very low storage cost. Additionally, data lakes’s own processing power means the latency in availability of data is drastically reduced.
Ease of access and collaborationData lakes make collaboration easier by bringing all data in one place. This avoids problems such as duplication of data or having to collect bits of data from multiple points, and navigating difference security policies for each.
Command over all formats
A data lake can process structured as well as unstructured data across all formats ranging from tables to text, audio, and video to binary files. All this data can be stored indefinitely even as more data is constantly added. This provides a data analyst with an always up-to-date reliable data store.
Open access to allFrom data analysts to data scientist or business intelligence analysts, people with different skills and tools can work simultaneously on the data stored in the data lake, performing different functions on the same data without moving it elsewhere.
How data lake compares to data warehouse
|Data lake||Data warehouse|
|Data acceptance||Stores structured as well as unstructured data||Can only store structured data such as tables|
|Capacity and cost||Can store any amount of data at low cost||Expansion of capacity needsmassive investment|
|Drawbacks||Dedicated tools are needed to mine and organise massive ammount of raw data.||Expensive and relatively restricted access, cannot support machine learning.|
Evolution of data lakes
Early rational databases
Data lakes are the latest stage in the long history of data management. In the earliest days, Rational Database was used to manage and analyse data. During the pre-internet the volume of data to be collected was small. However, with the arrival of the Internet, in late 20th century, the picture began to change.
Internet and explosion of data
The Internet led to an explosion of customer data. In response, the corporate world began to create multiple databases to classify and store data for various purposes. However, this led to many decentralized islands of data within an organization. Many companies failed to organize this data and gain insights from it. Thus, the need to better organize and analyse data led to the rise of data warehouses.
The emergence of data warehousesData warehouses brought all of an organization’s structured databases under one roof, enabling companies to get a complete picture of their data. Data warehouses made it easier for firms to audit and govern their data and to run limited analytical queries. However, the limitations to their storage capacity, high costs, and lack of capability to store unstructured data raised questions about the utility of data warehouses.
Tools to manage big dataEarly 21st century was the dawn of Big Data. Now there was massive data which could yield deep insights into the customer behavior, but it was unstructured, and could not fit inside one computer. This situation led to the rise of open source distributed data processing technology such as Hadoop. Hadoop worked together with an algorithm called MapReduce to store parts of a big database across many computers while still maintaining it as a single file. Hadoop also gave companies the ability to process unstructured data. MapReduce algorithm worked to split big computing tasks into smaller tasks which could be processed simultaneously on a group of computers.
After this, the Apache Spark arrived to considerably enhance the corporate big-data computing ability. The new program enabled data scientists to train machine learning programs at scale and process big data faster in real-time with features such as Spark Streaming. Spark is still used in modern data lakes to process data and develop machine learning models.[/et_font_size]
Challenges in data lakes
Rewriting the missing or corrupted data
Often when data is being written into the data lake, a problem with software or hardware leads only a part of data to be entered into the data lake, thereby making that data corrupt. In such cases, the engineer must now find and replace the missing data pieces. This job drains considerable time and energy. This issues is often solved by making the data entry transactional.
Data quality and reliability
Ascertaining the quality and reliability of data in the data lake is of prime importance. While issues with software progams can be easily detected, data issues can go undetected. Running the entire process with corrupt or inaccurate data can have a serious impact on your end results.
Data processingIn order to remain constantly updated, data lakes need to continuously work on combining historical, batch, and streaming data. Programmers have tried lambda architecture to solve this problem, but that entails creating and maintaining a separate code base for batch data and another one for streaming data. This is a rather difficult task. Many technology firms offer dedicated tools to make this process possible.
Data consistencyFunctions such as updating, mergers or deleting data need to be performed regularly on any database. However, carrying out these simple operations can be complicated on the data lake. Firstly there is no mechanism to ensure consistency of data, and secondly, even deleted files can remain on the system for as long as 30 days. The deletion of such files and other updating operations are ensured by enabling updating and deleting files on a single command using SQL.
As data lakes hold a massive amount of data, it is important for the query engines to be able to perform at scale. having a huge volume of small files can slow down the query performance. The same can also happen when storage space is repeatedly assessed. These issues are generally resolved by using compaction and data skipping techniques.
Metadata managementWhile data lakes are able to seamlessly process data, metadata can create some bottlenecks as the lakes grow. Advanced metadata management tools that process metadata in a manner similar to data itself, can resolve this issue.
Best practices for data lake management
Enter raw original data
Be mindful of saving the data in its totally raw, original format in the data lake. Do not perform any transformation function on the data when adding it to the lake. A skilled data scientist can generate insights from content within the data that might seem irrelevant to an untrained eye.
Restrict access to dataRestrict access to the data lake as per requirement. Companies can ensure view-based access to users where access can be controlled down to individual rows and columns using SQL.
Atomicity, consistency, isolation, and durability are typical features that make data warehouses a reliable option. Implementing transactional guarantees, scalable metadata handling and batch streaming unification can bring the same reliability to data lakes.
Curate the dataThe data lake is the prime source of data for data analysts and data scientists. Hence, it is essential that the data entering this central data store be cataloged properly and curated regularly. It is important to tag new data sources with relevant information so that they can be classified and discovered with ease. There are a number of software programs that can make it easy for users to classify data.
Data lake tools
Apache Spark is a unified analytics engine used for rapid distribution and processing of data in a data lake. It has given rise to the largest open source community in big data today.
The Simple Storage Service (S3) by Amazon provides cost-effective storage and security service or your data. The program is noted for easy data management and cloud support service including the facility to run query in place service for analytics.
DatabricksDatabricks unified data analytics platform is dedicated to running SQL queries in data lakes. It is built to process data at scale and facilitate collaboration.
Dalta Lake is a program that helps strengthen data lake architecture and security. It brings ACID transactions and scalable metadata handling to data lakes.
Azure setup Spark environment and build AI solutions for data lakes. It supports a wide range of programming languages, data science frameworks and libraries
PrestoPresto is a distributed query engine for big data created by Facebook. It enables running SQL queries on data lakes at scale.
Leverage the power of data
Crafsol helps businesses to deal with data of any size, form and flow through effective Data Lakes. Developers and data scientists can leverage the power of Data.
We help our clients empower their developers, data scientists, and analysts with the right tools to leverage best results from data lakes
Services we Offer
Strategy & Roadmap Access business goals and accordingly create reference technical, logical, and physical architecture.
Prototyping & Tool to Evaluate/ prototype tools and technologies to find the best-fit solution.
Data Integration, access & services Integrate existing data sets and tools with advanced solutions.
Develop, deploy, and enable seamless adoption of data lake across the organization.
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