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 Does your company generate massive amounts of data? Big data can help you produce useful business insights so you can improve your products and services, while making your internal processes more efficient and increasing profits. Big data requires powerful technologies though in order to store and analyse it. High quality data lakes may be exactly what you need.

A data lake is one way that companies store data. You can compare it to an actual lake though maybe a sea would be a better example multiple streams feed into it.  Data is stored in a similar way in a data lake. Follow the article to learn reasons to choose data lake architecture for your business data.

Data lake – definition

Data lakes store a huge volume of data in its native, raw format the data stored in a data lake may differ a lot and still be kept together in it. No matter if stored data is structured, unstructured or semi-structured, it can be loaded into a data lake in the original format. That means that you can keep all information in one location, no matter what its format is or if you need it to perform specific tasks (for example, reporting or analysis). 

There are many solutions for storing big data and each of them has advantages and disadvantages. Before we go through the details, take a minute to understand the advantages of storing data in data lakes. 

How to create effective data lake? See our big data engineering services page and check how our knowledge can help your business

The advantages of developing a data lake for your company

What makes data lakes special is that you can store all types of data in them in fact, any data you want. That gives you a lot of flexibility, as you have access to all the data you need (even old data, or information you once thought useless and unimportant). 

If you decide to use a data lake to store your data, you will quickly realize that there is a value in data of all types and data lake architecture allows you to derive this value. With this approach to storing data you can easily use data earlier stored in different systems and databases for complex analytics in order to improve innovation level in your company. A data lake is the opposite of silos architecture thanks to that, analysis is simpler and faster.  

There are almost no limits for managing and processing your information stored in data lakes. There are many ways to query the data and plenty of tools you can use to gain insights for your organization. For example, you can use Machine Learning and Artificial Intelligence to benefit from predictive analysis.

Data lake layers

Well now, you may think of a data lake as a huge container in which data is stored without any order, but this solution lets you divide the lake into separate layers. Generally, three to five types are mentioned, although they can be named in different ways by experts. Each has a different purpose. Here are some of them.

The ingestion layer of a data lake

This is a layer in which raw data is ingested from various sources (such as applications, IoT devices, etc.). The point is that data should be ingested as fast and efficiently as possible that is why on this level data cannot be modified but remains in its native format. Raw data is organized in folders. On this layer, stored data is not yet prepared to be used for analysis or reporting; that is why granting access to a big group of users is pointless and should be avoided. 

The curated data layer of a data lake

On this layer, the user needs to choose the purpose for a given piece of data and the right form for it. In order to be processed for creating insights and reporting, this data must be transformed (cleaned and mastered) in the format chosen by the user. Structured data sets can be later used for analysis. Both unstructured and structured information may be stored in various types of files. 

The application data layer of a data lake

This layer runs queries and analytical tools on data that has been structured, which can be done in real-time. At this stage, data sets are processed with any needed business logic and used by analytical applications.

The sandbox data layer of a data lake

Here data can be used for experiments this is optional and often serves as a data science workspace. This one is recommended for advanced specialists of data analytics or data scientists.

In some articles you can read about layers for storing temporary files or insights layers. Which should you use? This is an individual matter. Our consultants can advise you on the best solution after analysing your business needs.

Designing data lake architecture – what do you have to remember?

Data lakes are generally a highly scalable solution that provides low-cost storage space. Certain matters should be taken into consideration when designing a particular data lake for your company. Layers are vital components, and they should be designed carefully. Each layer has a different purpose, so the requirements vary. The ingestion layer has to support multiple data sources (like social media, databases, IoT and others) and ingestion modes (batch, real-time), while being able to store any type of data. It would be great if a solution were flexible enough to easily support possible, new data sources.

Data lake security

One of the most important considerations is security you need to protect your data and prevent possible leaks. The easiest and most obvious method of ensuring safety is to secure your data lake from unauthorized access. Special precautions should be applied on each layer.

Governance and management of data in a data lake

Data management is also very important. In time, it will become crucial to monitor the operations performed in the data lake in order to measure and improve the performance of this solution. You will need to use metadata to ensure that all processes run efficiently and enable users to easily search and gain information about datasets in the lake. Adding extra descriptions about data’s purpose and operations will make your analysis more effective.

You need to be sure that data acquisition and transformation is automated in order to deal with huge amounts of various types of data in a short time. There are many techniques and tools you can use to improve the process of producing insights from business data. Perhaps you could benefit from Artificial Intelligence? Cloud-optimized data lake architecture may be a good idea cloud solutions are all about scalability, great performance, security and flexibility. 

A well-designed data lake will support the systems and tools that you’re using at the moment. It should enable cooperation between users, so sharing analysis should be quick and easy. That often makes it possible to avoid duplicating efforts in producing insights. This will result in your teams working more efficiently. It is important that data lake architecture be tailored to a specific industry. Thanks to this, you get a business solution that suits your company’s every need.

Why should your company use data lakes?

Unlimited access to data is very important for most businesses nowadays. Data Lakes ensure data availability at all times for users, no matter where they are. This affordable solution supports not only SQL, but also other languages, which makes it a better choice than data warehouse if advanced analytics is required. 

They can be used by companies which generate a lot of types of data and need solutions to store and process it efficiently. Some data may not be valuable at the moment, but might become so in the future thanks to data lakes, your data can be easily used after a long time. The possibility to store data in native formats is great for any business, as it creates an opportunity to take advantage of new technologies in the future by having data in its native format, it will be easy to use it with new analytics tools in the future.

Are you wondering if a data lake is the best solution for your business? Using it you can perform complex analytics and benefit from machine learning, which will certainly improve your company innovation level. Contact our experienced consultants and tell us more about your company’s needs. 

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  • Laura Kszczanowicz
  • Robert Michalak

    Robert works as a Data Engineer in GCP platform. His past experience with Data Warehouse solutons is useful in cloud to bring valuable data for customers. Robert's main technology stack: BigQuery, Python, Kubeflow, Github, Airflow, and more, because it's constantly changing and developing. To recharge batteries he spends free time in the mountains riding on the enduro bike.

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