What is a data lake and why is it important?

The data lake is a very large data store that contains data from a wide variety of sources in its raw form. It can contain both unstructured and structured data and can be used for big data analysis

The term Data Lake means a very large data store. Unlike normal databases, it contains data in its original raw format. The data lake can be fed from a wide variety of sources. Data can be structured or unstructured and does not need to be validated or reformatted prior to storage. In addition to text-based or number-based data, the data lake can also record images, videos, or other data formats. Structuring and, if necessary, reformatting the data in question only takes place when the data is needed.

Due to its enormous amount of information, a data lake can be used for flexible analysis in the big data environment. Data from various sources can be used for many different applications and analyses.

Main features of a data lake

The data lake must provide certain basic functions to meet the requirements of information-based applications. A wide variety of data and data formats, both structured and unstructured, must be able to be stored in the data lake. This avoids distributed data silos. To allow for the most flexible use of data, the common structures and protocols of the database systems and database applications of the Big Data environment must be supported. Access to data must be protected by powerful role-based access control to meet data protection and security requirements. In addition, data encryption must be used. Mechanisms for backing up and restoring data must also be provided.

Comparison between data lake and data warehouse

The terms data lake and data warehouse are often used in connection with storing and serving large amounts of data. Although both the data warehouse and the data lake are capable of storing large amounts of information and making it available for evaluation, they differ fundamentally in their concepts and in the type of data storage. The data warehouse combines data from different sources and converts it into formats and structures that allow for direct analysis. Data lake, on the other hand, fetches the data from different sources in its raw format and also stores it in an unstructured way. It is irrelevant whether the data is relevant for further analysis. The data lake has a flat hierarchy and does not need to know the type of analysis to be performed later to store the data. Searching, structuring or reformatting only takes place when the data is really needed.

The data warehouse usually stores metrics or transactional data. Unstructured data such as images or audio data is not stored in the data warehouse. The data lake accepts all information in its original format that is offered to it. Because the data lake keeps the data in its original format, it can be used much more flexibly than the data warehouse when requirements change. Data can be converted into completely new structures and analyzed using new methods.

What advantages does it offer?

A data lake offers many benefits to businesses as it allows you to collect data from different sources quickly and easily. Some of the main advantages are:

• Flexibility: Data is stored in its original format. They do not need to be structured or pre-processed. This allows companies to use their data more flexibly and quickly to make data-driven decisions and develop predictive models;

• Scalability: Data lakes are designed to be scalable. This allows companies to expand their data volumes quickly and easily. They don't need to change their infrastructure separately;

• Collaboration: A data lake allows companies to bring together and leverage data from different departments and disciplines;

• Big data processing: A data lake is often used in conjunction with big data technologies such as Apache Hadoop or Apache Spark. This is done to ensure the processing of large amounts of data;

• More openness: Data lakes don't lock you into a specific format. You can store structured, unstructured, and semi-structured data as you wish. This includes, for example, streaming data, videos, images, binary files, social media and other marketing data. This openness in terms of formats makes your company more agile in general;

• More robust: Because data lakes can handle a variety of formats, they are more robust than other data storage concepts. The storage environment has fewer requirements and parameters to consider and is therefore less prone to malfunction;

• More information: Data lakes form the foundation for new business-critical technologies such as machine learning, big data analytics, and predictive analytics. In this way, companies can recognize hidden patterns, for example where there is still untapped potential for process optimization, or make predictions about how markets will develop. This is a crucial competitive advantage;

• More consistency: Data silos exist in many companies. This means that data that is actually related to each other is kept separate from each other. This often leads to duplication of datasets. This leads to significant productivity losses, for example, because different departments do not cooperate with each other on the same database. But it also creates compliance issues, with different silos of data using different IT security policies.

• More Accessibility: Data lakes make it easy for your users to ingest new data and retrieve data already stored using self-service tools. This contributes to a democratization of the data culture in the company. More employees can more easily make data-driven decisions;

• Data Security: A data lake enables companies to comply with data security and privacy regulations. This is done by being able to protect and control your data;

• Time and cost savings: By automating integration processes and being able to store data in its native format, a data lake company can save time and money spent on manual data preparation and merging.

Overall, it offers companies the opportunity to use data flexibly, quickly and jointly. This allows you to make data-driven decisions and work together securely across all departments.

Cloud versus On-premise solutions

In the early days, data lakes were mostly run on premises. In the meantime, however, the trend is towards the cloud. All major cloud computing providers have corresponding solutions at their fingertips. On AWS it is Amazon EMR, on Microsoft Azure Azure HDInsight fulfills this task and Google is represented with Google Cloud Dataproc. Such solutions usually rely on very well scalable big data platforms and can be integrated with Hadoop and Spark.

Historically, this development makes sense. With the advent of professional cloud solutions for businesses, online storage space has become cheaper and cheaper. Furthermore, providers have made cloud-based data lakes more attractive by continuously expanding their offering with useful functions. Competing on-premises solutions couldn't keep up with this pace of innovation because most internal IT departments had only limited know-how and manpower. By outsourcing data management to the cloud, the internal team was relieved; employees were able to focus more on their core business.

This trend continues to this day. Cloud data lakes are always technically up-to-date, are constantly being expanded with resources and tie up fewer employees. This is especially true if cloud services are obtained through a managed service provider (MSP). Here, more than storage space is made available for your business. You will receive a solution individually tailored to your needs with a personal contact person and support team. MSP also supports change management. Large cloud providers generally cannot afford this degree of individualization.

Some lake applications

Data lakes are used in a wide variety of industries. Here we have 03 examples:

1) Media Industry

Streaming services store large amounts of user data in data lakes. Your analysis allows us to suggest new songs or suitable series for users based on the content they have consumed so far. By allowing the user to spend more time on the platform, the company is able to sell more advertising space;

2) Telecommunications Provider

Providers in the mobile communications industry are struggling with the fact that customers frequently switch providers. These fluctuations can be controlled with predictive analytics models. Data lakes provide the necessary data for this;

3) Financial Industry

Investment firms use machine learning algorithms to better assess the risks of a given portfolio. For this analysis to occur in real time, large amounts of data must be stored in data lakes.

What recommended best practices should you be aware of?

Use the following best practices to optimize your data lake operation:

1) Save your data directly

Resist the temptation to prepare and structure your data before it enters the data lake. The decisive advantage of the solution is precisely that such preparation is not required. Trust that your data can be further evaluated by powerful search algorithms and machine learning;

2) Be aware of privacy requirements

Personal data must first be anonymized before adding it to your data lake. This is necessary to meet the requirements of the General Data Protection Act. Be sure to fully decontextualize. Several recent experiments have shown that data, although anonymized, could later be attributed to the appropriate person by data scientists. This additional effort is also worth it because it is to be expected that data protection requirements will increase in the future.

3) Use Access Control Lists

For many data lakes, user rights are still assigned based on roles. Enable more management options here by introducing so-called access control lists. Access control lists do everything that role-based solutions can do, but they also offer group management and can handle inheritance of hierarchies. This gives your administrators more options for action.

4) Catalog your data

When moving your data into the data lake, you must utilize data cataloging and metadata management tools. Later, this will facilitate the use of analytics and self-service applications.

The article has been written by Lenildo Morais, Master in Computer Science, University Professor, Researcher and Project Manager