Benefits of Data Lakes for SaaS: 2021⁠–⁠2022

28 December 2021

Jason Bright


According to a study by Statista, the global BDA market is estimated at about $215 billion as of the end of 2021. This figure reflects the level of business involvement in learning new data processing models. Information today is valued above all else in commercial organizations. The profit of any company is directly related to its analytical potential. The more information an organization extracts from the World Wide Web, the better it analyzes and uses it correctly, and the higher the final income. 

Data is a weapon of mass destruction in the right hands. If they are structured and processed timely, you can gain benefits and increase profits. The main things in this business are a wise approach and good technical implementation. Rocketech experts researched trends for 2022 and found a significant dependence of most forecasts on the development of data lake technology. In this article, we’ll talk about the differences between DB and DL, their roles, and their impact on SaaS. 

Data Lakes, SaaS, and How They are Connected

What are data lakes? These are unstructured information stores in which files, text, content, tables, and everything else are displayed in a “raw” form. Unlike standard databases, access to data is open at once in lakes because digital information is literally “smeared” with a thin layer over the virtual surface. The main difficulty in finding the desired fragment is that it requires special skills and tools. Now it is not a problem because most high-level analysts are mastering this new technology now and introducing their ideas into IT systems.

Let’s take a closer look at the difference between DL and DB:

Data lakeDatabase
Data is stored in open formData is stored in a standard form
Information is processed only on requestFiles are processed and converted
Used for analysis Used for work
New technologiesClassic model

As you can see, the main difference between them is the method of use. 

DL is currently the best solution to implement in machine learning products. These include modern analytics systems, cloud-based customer services, and SaaS. Data lakes are excellent sandboxes where you can analyze information, build complex models, test them, and compare performance. In the near future, when AI becomes a mandatory component of SaaS systems, DL will fully unleash its potential and take SaaS to a new level in both productivity and quality of work.

Data Analytics Challenges: Processing Speed and Volume of Information

Data analytics competitions in 2022 will imply all the same tasks that developers and scientists set over the past decade. Despite the progress in research, the main problems are not solved, although a huge budget and the mental resources of thousands of experts are invested in their study. Today, the key obstacles to the effective implementation of new technologies are:

  • Speed ​​of information processing. Modern systems require enormous computing power. It affects how quickly the data is converted into a readable form, how much volume per second the tool can pass through itself, etc. 
  • Limited storage. In the past 3 years, the world has faced a shortage of electronic components, which influenced the release of server solutions, large drives, etc. The lack of space is due to the huge amount of information received.
  • Difficult analysis. To work with data lakes, special tools are needed to extract the necessary arrays of digital information and transform them. 
  • Lack of resources. Complex solutions require highly qualified specialists, time, and computing power. The problem is that often limited resources slow down data processing.
  • The reliability of the data. The information flow is so large that it is almost impossible to control it. A kind of dump appears in which it is difficult to isolate truthful and relevant data.

All of the above are data analytics challenges for scientists around the world. Also, these problems set the development vector for DL-related IT solutions for the next decade. Who knows, maybe in 2022, we will see the progress that will turn the world inside data science upside down.

How DL Unleashes the Potential of SaaS 

SaaS stands for “app as a service”. This term applies to most modern commercial and industrial cloud systems. In fact, this IT product is a web app configured individually for each customer according to their field of work. SaaS can act as an analytic tool or an administrative dashboard with analytical functionality. Therefore, it may become the largest source of information, a data lake. 

What does it mean for businesses? Data lakes can analyze, structure, and provide relevant information for:

  • Building a content strategy
  • Developing a marketing plan
  • Optimization of information
  • Targeting by interests or queries
  • Expansion of the range of goods or services
  • Risk assessments of new ideas or work models.

Any SaaS solution with analytical functionality can use data lakes and a database simultaneously. Integration between them helps to automate information exchange and use cloud resources effectively by collecting information from the network and sorting it by storage.

Benefits of DL for SaaS in 2022

Let’s imagine a classic SaaS solution. For example, a CRM system for a trading business. How it works:

  • Deploying the program on a server where the interface is adapted to the user’s device.
  • Integrating the necessary operational and analytical functionality into the IT product.
  • Synchronizing the system with the client database or creating a new one from scratch.
  • Connecting dependencies and writing relationships between units and cells. 

If we need an advanced solution, we introduce tools that:

  • Use information from the database for analysis, which is prescribed by algorithms.
  • Process incoming and outgoing data, structuring and updating them.
  • Based on the analysis results, give out relevant schemes with proposals for optimizing work.
  • Use many external sources for preservation and analysis.

As a result, we get a working scheme that accumulates and structures information, processes it, and prepares proposals for optimization. As a result, this complex of integrations provides:

  • An inexhaustible source of data for analysis, fueled by thousands of streams from Internet resources.
  • A sandbox to build and test information models. 
  • Raw data that hides user info and digital footprints.
  • A unique hybrid system for storing and processing information in the cloud.

When DL is integrated into SaaS, there are only positive changes in the overall effectiveness of the tool. The analytics department receives up-to-date data, processes it, and transmits it to marketers who also use this system for their purposes. The entire chain of interconnection optimizes the business workflow with the right approach. Based on the data obtained, it is even possible to generate ideas for development using AI, but it is now more fantasy than reality.

Inside Data Science: A World of Unexplored Knowledge

Like a sponge that absorbs life-giving moisture, data lakes absorb information from thousands of sources. They store it in a “flat” form, opening up unlimited access to the analysis of any element. Using this technology, you can do crazy things: compare and process tons of data in real time, connect AI to work on extracting useful information, and predict the development of the IT market, e-commerce, or marketing. In general, the potential of data lakes is not limited, but they are not yet prevalent, so it is difficult even to imagine their maximum potential.

Let’s Summarize

Immersion inside big data is not so deep now since it is at an early development stage. According to forecasts, data lakes will soon replace traditional databases, expanding their structure and optimizing work tools. But it is only a logical vector of development, and we would like scientists to adhere to it in the future. Data analytics competitions in 2022 will raise the bar even higher for all DL-based products. SaaS will be taught to seamlessly integrate with platforms like Azure AWS to maximize the benefits of thousands of terabytes of free information. 

Rocketech experts monitor the development of the IT market, innovations, and promising technologies so that you receive new interesting facts or ideas for your projects. We develop complex solutions in which we integrate DL at a customer’s request. For detailed information about our services, you can contact our manager.

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