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An Introduction to Data Lakes

Data Lakes

Data lake architecture, a flexible centralized storage area, plays an essential role in many IT projects. It assists Big Data projects like machine learning and handles streaming social media output better to analyze market sentiments.

Most importantly, many professionals find it key in solving complex problems. Journalists identified tax evasion in 2016 by using a data lake database that a graphing interface could access. 

This article will introduce the data lake, its management, plusses, and minuses to understand and apply this valuable tool well.

What is a Data Lake?

A data lake describes an elastic container with unstructured, semi-structured, or structured data and the means to manipulate this data through code, a type of computerized object. This technology lets data science experts retrieve raw data quicker and more effectively. Users can reframe data sets to answer a large variety of related or unrelated questions, as the user organizes data upon retrieving it. 

It grew out of a need in the mid to late 2000s to gain business insights from mobile phone usage, social media, videos, and sophisticated behavior tracking. The available architecture, data warehouse, could not handle this voluminous and quick-moving data. In contrast, it excelled in real-time non-relational data, like video streams or the time-series information from a stock exchange.

What is Data Lake Management?

Data lake management covers the strategies, procedures, processes, and activities a business does, from data lake ingestion to deletion. Data lakes can be tricky to manage. While they do not require as much data management to figure out how to organize data entry, forms, and reports, they need some structure to have a reasonable data quality.

Managing data lakes still requires data governance – activities that give authority and control over it. Also, management benefits from some standardized metadata, data with context about the data set/s, such as owner, topic, and date created. 

Data governance and metadata tagging provide a light-handed data lake management to keep up with the data ingested and make its data more usable. Good, automated tools that find and catalog data sets play an essential role in retrieving and preparing that data for use. 

What Are Data Lake Advantages?

Data lakes offer many advantages for technology professionals:

  • Cost: It cheaply holds bulky data for as long as you need. Much of the setup and programming uses open-source code, which is free. Also, maintaining data in pure raw form requires little expense, as you don’t need to transform extracted data into some sort of structure before loading it into the system.
  • Variety: It can hold any format, including videos, pictures, audio, and or other databases, as well. Transferring any data just requires a connection between the data lake and the user. It can quickly load from many diverse data generators – like chatbot transcripts, computer system backup or image files, or Zoom call sessions.
  • Scalability: It make it easy to change what kind of data it ingests, how much, and when. Connecting another data source or limiting the streaming data can be done immediately without affecting existing functionality. Storage on the cloud can shrink or expand as required.
  • Agility: A data lake can start ingesting data right away and requires less setup than a data warehouse. Data can be modified or consumed on the fly immediately. Data sets in it are highly available to people or applications. 
  • Innovation: Data lakes promote creative thinking when searching for patterns and solutions. Think of it as a sandbox to explore possibilities and play with the data. For example, the same data sets about the traffic on city roads informing commuters about the shortest route home could be reframed for the city to plan road maintenance.

What Are the Challenges?

Data lakes come with drawbacks and misunderstandings that discourage their use. Challenges include:

  • Data Quality: Data in data lakes continually change. As a result of this data fluidity, a person can easily misread the information to be inaccurate or incomplete instead of associating a value with a particular moment in time. For example, a user retrieves a data set today but gets a different data set tomorrow with the exact search done today because the data lake system purged some of the data 5 hours ago.
  • Hard to Use: Just sampling data directly from it takes some programming and mathematical skills. The user assumes responsibility to organize data in a purposeful way. Often, this means writing algorithms to transform the data for business purposes or using another purchased application – like a graphing tool to give – a meaningful data context.
  • Concrete Business Purpose: A business runs by and in a particular context, but data lakes are contextless. So, they make inadequate tools, by themselves, to do financial transactions, track inventory, keep track of customer relations or any other kind of operations. Instead, they helps identify the activities and insights towards adapting the business to the future.

Final Words:

Data lakes offer an agile architecture excellent for high volumes and all sorts of data types that generate very quickly. Traditional databases just fall short of solving complex problems, experimenting with data, and seeing unobvious but logical patterns. 

Using it with another system like a data warehouse or graphing utility empowers technology professionals. Data lakes coincide with better results in Big Data projects. 

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