Data Lake

Comparing a data lake with a data warehouse

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Data volumes are increasing at an accelerated pace every year. The number of streaming data has increased significantly, and unstructured data is increasingly eclipsing its structured counterparts. As a result, a business that works with large databases has to process information before loading, which requires a lot of time and effort. But all the same, in the end, some of the information is lost, but or could be useful in the future. And an innovative product is called upon to solve this problem – a data lake, that is, a data lake. What it is? How effective? What are the differences from classical data warehouses?

Introducing the Data Lake

A data lake is a large storage facility where all data is stored in its original, raw and unorganized form. If necessary, the user will “fish out” them and only then proceed to processing. To access them, the user should know:

  • how much data needs to be “caught” and when;
  • applied methods of analytics;
  • certain types of data and the sources they need.

It is impossible to implement all this in a classic data warehouse, or the execution time of these works will be very long. And in modern business, with its extremely active market and constantly changing conditions, this is unacceptable.

In a simpler language, answering the question of what it is, a data lake can be said to be a storage for videos, magazines, books, PDF files, Word documents, photo albums, audio recordings, and any other data that does not have a structure. It doesn’t matter what size or format they are. It doesn’t matter what source they come from: CRM or ERP systems, financial institution software, smart gadgets, sensors, product catalogs or other systems that the company uses.

You can extract information into a traditional database using a special template. Also, users can analyze and process information directly inside the data lake using software: compile analytics, structure, distribute, etc. Work with BI systems is also allowed here. They are indispensable when you need to solve deep analytics problems, model forecasts and present the information obtained in a visual, easily accessible form.

Done well, a data lake will enable business analysts and technical users to query the small but flexible sets of data needed in a given time period. This will lead to a significant reduction in the time spent on query execution. This is especially true in today’s business environment, where you need to constantly integrate small data with big data. No matter how reliable and powerful an application is, it alone cannot handle everything. This is where the data lake comes to the rescue.

The unique features of the data lake have contributed to the fact that it has become used in many areas of business, from conventional financial management to risk management.

What is the difference between a regular database and a data lake

The main difference between a data lake and traditional databases is its structure. If only strictly structured information is preserved in the former, then everything can be thrown into the lake without wasting time even on elementary systematization and ordering.

As an example. There is such a description of potential buyers of goods or services: “Housewives 30-50 years old with a good level of income, married, with children. Single women 35+ who have a certain status in society. Men aged 35-60 without a family, without children, holding leadership positions, with stable earnings. A similar description with unstructured information is placed in a data lake.

But if it were to be loaded into a typical database, it would be necessary to draw up portraits of the target audience, clearly structuring them according to the following criteria:

  • floor;
  • age;
  • marital status;
  • the presence of children;
  • position in society;
  • income level.

That is, all incoming information must undergo a thorough analysis, on the basis of which the structure will be compiled. And only after that it will be recorded in the cells strictly designated for each request. Here it will be possible to create an algorithm that works with each of the cells, because it is reliably known what kind of information is in each of them. All of these jobs take a lot of time. But at the same time, no one knows in advance whether this information will be needed in the future. If it is not required, the time and effort for structuring will be wasted.

Unlike a traditional database, any information can be thrown into the data lake, without the slightest refinement, systematization. But if there is a request from the client for it or it is required in the work, it can be extracted, analyzed and structured. But the “source” will remain stored in the database without measurements. This solution is very convenient in practice. In the future, it will be possible to extract it again and already structure it according to other criteria, those that will be relevant at that time.

Many experts compare a data lake with a computer hard drive, while a regular database is an Excel spreadsheet, where all this information is placed in its own cell. But the data volumes are much more impressive.

Other differences between a traditional database and a data lake

Among other aspects that distinguish the traditional database and the data lake, it is worth highlighting:

  1. Increased flexibility . In the classic version, it is minimal. Even at the structuring stage, it is necessary to establish key factors, take into account data types and provide an appropriate structure for them. And if additional information appears, it cannot simply be added to the tables. It will be necessary to rebuild the structure. The flexibility of the data lake is maximum, which has a positive effect on the data quality (quality of storage management). Nothing needs to be defined and thought out in advance. If you need to make additions, you just need to upload them there.
  2. Data utility level . No one will waste time and effort loading information into the repository that seems unimportant at a given time. It is simply removed, thrown out of consideration. All work is carried out on data that is really important for business at a certain point in time. But what will happen if the situation changes and information that seemed important will no longer be needed, but secondary, on the contrary, will be required. And it’s gone, it’s been removed. You can upload any information to the “lakes”, even in case “what if it comes in handy someday”. It only takes a couple of seconds.
  3. Stored data types . Only structured information is loaded into a traditional database. These are tables with text, numbers, placed according to a pre-planned structure. You can upload videos, photos, audio messages, text, graphic files and any other electronic material to the data lake.
  4. Data availability . Here the conventional database has the palm. The information that is stored in it will be read not only by a narrow specialist, but by a business analyst or any employee of the company. The information that is extracted from the lake is fragmented and it is impossible to navigate it. It needs structuring, which cannot be done without the help of analytical specialists, in particular a Data Scientist.
  5. Issue price . Databases designed to store large amounts of information will be expensive. Complex analysis, structuring, building a multi-tiered data system architecture costs a lot of money. Storing information in a data lake will cost several times less. Here, you only pay for the space your information occupies.

Looking at all this, it can be argued that a traditional database is well suited for storing important information that your business will use on a regular basis. The ones you always have to keep close at hand. But if your goal is to save data that may be required in the future, if there is no desire and time to mess with their structuring, then it is better to “throw” them into the data lake.

A data lake will be indispensable if you need to perform flexible analysis of information to develop a future strategy. Thanks to them, you can collect a huge amount of information. Then it remains only to compare individual data using machine learning tools, create hypotheses and business forecasts based on them. They allow you to perform voluminous research, obtaining the most accurate, detailed picture that will be useful in the work of business analysts and will give real results.

Benefits and risks of using a data lake

If we talk about the benefits, then the data lake has a lot of them:

  • fully copes with advanced analytics and the so-called “ionization” of the product;
  • provides economical data storage;
  • with long-term use, the cost is significantly reduced;
  • provides an instant response to changes;
  • is characterized by high flexibility combined with cost-effective scalability;
  • you can store content from various sources;
  • users from any corner of the planet can access the lake.

But, despite a number of significant advantages, there are risks. In particular, one cannot be sure of the reliability of the analysis results from third-party analysts: there is no data on where the original information was taken from. The disadvantages of the data lake also include questionable information. No one controls when they are poured, which made it possible to reduce the cost of their storage. In view of this, there is a risk of turning Data Lake into a “swamp“.

To minimize the shortcomings and ensure the reliable safety of data, debugging the process of managing them – data governance. This strategy allows you to set the quality of the information before uploading it. It will discard sources with deliberately false or unreliable information. It will ensure the arrangement of access rights for the specified categories of employees, will be able to check the specific parameters of incoming information.

In order to improve the efficiency of the data lake, it is recommended:

  • combine it with other components of the company’s infrastructure: databases, cloud services, the Internet of Things, etc.;
  • do not litter the data lake: in most cases it is easier to organize several separate repositories for each category than to throw everything into one large database;
  • check the quality of the metadata and the origin of the information, which will maintain sufficient confidence in them;
  • create a team of data engineers, analysts and developers in your company, providing them with access to the database and tools: work with information of incompetent employees is excluded;
  • Prevent leakage or loss of information by properly organizing security: competent access control management, secure perimeter, recovery, backup storage, etc.
  • All this will ensure the stability, reliability of the data lake, its ease of use and will not allow it to turn into a swamp.

Summing up

But, despite the versatility and high benefits for any business, organizing a data lake is a complex process that requires a competent approach. It is necessary to proceed from what is available, not what is required. Initially, you should evaluate the prospects, take into account the costs of implementation. This is very difficult to do without sufficient knowledge and practical skills. If you need help, please contact Xelent. All questions about the terms of cooperation and additional information about the data lake can be obtained from the company’s managers by contacting them by phone or via the feedback form.

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