Top 5 Differences between Data Lakes & Data Warehouses

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  • Posted in Data Warehousing
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  • 9 mins read

According to Google, “Big Data” has been on the rise for several years and has really taken off in the last couple of years. The goal of this article is to help you understand the distinctions between Data Lakes and data warehouses so you can make an educated decision about how to handle your data.

Those of us who work in data and analytics, have undoubtedly heard the word, and when we start talking about customers about big data solutions, the conversation automatically shifts to Data Lakes. Customers, on the other hand, frequently haven’t heard the term or have a poor understanding of the concept of what it entails.


Data Warehouse

Let’s see what Data warehouse is defined as.

“A data warehouse is a centralized repository of integrated data from one or more disparate sources. Data warehouses store current and historical data and are used for reporting and analysis of the data.” This is a very high-level definition of a data warehouse that explains the aim but not how it is accomplished.

Take a quick look at the image below:

I’d go on to say that a data warehouse possesses the following 3 characteristics:

  1. It’s an abstracted representation of the company’s operations, arranged by subject.
  2. It has undergone a lot of transformation and has a lot of structure.
  3. Data isn’t entered into the data warehouse until its purpose is determined.

Data Lake

Microsoft is often recognised with coining the phrase “Data Lake.” A data mart (a subset of a data warehouse) is compared to a bottle of water. While a Data Lake is more like a body of water in its natural state, it is “cleansed, packed, and arranged for easy consumption.” The lake receives data from the streams (source systems). The lake is open to the public for inspection, sampling, and diving.

This is also a quite hazy definition. Let’s add a couple more Data Lake characteristics:

Source systems are used to load all data. There isn’t a single piece of information that isn’t considered. At the leaf level, data is stored in an untransformed or virtually untransformed condition.

Next, let’s highlight five key differentiators of a Data Lake and how they contrast with the Data Warehouse approach.

1. Data Lakes Retain All Data

A significant amount of effort is spent researching data sources, understanding business processes, and profiling data throughout the building of a data warehouse. As a result, a highly organised data model for reporting has been created. Making decisions about what data to include and exclude from the warehouse is an important element of this process. Data may be excluded from the warehouse if it isn’t used to answer particular questions or in a defined report. This is frequently done to simplify the data model and save space on the pricey disc storage that is required to keep the data warehouse running.

Data Lake, on the other hand, keeps all of the data. Not only data that is currently in use, but also data that may be utilised in the future and even data that will never be used simply because it MIGHT be used in the future. Data is also saved indefinitely so that we can perform analysis at any point in time.

Because the hardware for a Data Lake is typically different from that utilised for a data warehouse, this strategy is conceivable. Scaling a Data Lake to terabytes and petabytes is relatively inexpensive because to commodity off-the-shelf servers and low-cost storage.

2. Data Lakes Support All Data Types

Data warehouses typically contain quantitative indicators and the properties that describe them, as well as data derived from transactional systems. Web server logs, sensor data, social network activity, text, and photographs are all examples of non-traditional data sources.

New applications for various data kinds continue to emerge but consuming and storing them can be costly and time-consuming. These non-traditional data formats are embraced by the Data Lake concept. We keep all data in the Data Lake, regardless of source or structure. We retain it in its natural state and only modify it when we need it. This is known as the “Schema on Read” technique, as opposed to the “Schema on Write” approach in Data Warehouse.

3. Data Lakes Support All Users

Most businesses have 80 percent or more “operational” users. They want their reports every day, as well as to examine their key performance measures and slice the same set of data in a spreadsheet. The data warehouse is usually the best option for these consumers since it is well-structured, simple to use, and understand, and it was designed specifically to answer their problems.

Do a little additional data analysis in the next 10% or so. They use the data warehouse as a source, but they frequently return to source systems for data that isn’t in the warehouse, and they occasionally bring in data from outside the company. Their preferred tool is the spreadsheet, and they frequently produce fresh reports that are disseminated throughout the company. Their go-to data source is the data warehouse, but they frequently push beyond its limits.

Finally, a small percentage of users conduct in-depth research. They may develop entirely new data sources as a result of their research. They combine a variety of data sources to generate whole new questions to be answered. These users may use the data warehouse, but they frequently disregard it because they are often tasked with exceeding its capabilities. Data Scientists are among these users, and they may employ advanced analytic tools and capabilities such as statistical analysis and predictive modelling.

4. Data Lakes Adapt Easily to Changes

The time it takes to change data warehouses is one of the most common complaints. During development, a significant amount of time is spent getting the warehouse’s structure just perfect. A strong warehouse design can adapt to change, but due to the complexity of the data loading process and the work done to make analysis and reporting simple, these changes will require development resources and time.

Many business aspects can’t wait for the data warehouse team to change their technology. The concept of self-service business intelligence arose from the ever-increasing need for speedier replies.

Users in the Data Lake, on the other hand, are enabled to go beyond the structure of the warehouse to explore data in creative ways and answer their questions at their own pace because all data is stored in its raw form and is always accessible to someone who needs to utilise it.

If an exploration’s outcome is found to be valuable and there is a desire to repeat it, a more formal schema can be applied to it, and automation and reusability can be built to assist expand the results to a larger audience. If it is found that the result is not helpful, it can be discarded without causing any changes to the data structures or consuming development resources.

5. Data Lakes Provide Faster Insights

This final distinction is a product of the previous four. Because Data Lakes contain all forms of data and allow users to access data before it has been processed, cleansed, or structured, they can get to their results faster than with a standard data warehouse.

This early access to the data, however, comes at a cost. Some or all of the data sources necessary for an analysis may not be covered by the work done by the data warehouse development team. This puts users in control of the data, allowing them to explore and use it as they see fit, but the first tier of business users I mentioned before may not wish to do so. They’re still only interested in reporting and KPIs.

These operational report consumers will use more structured representations of the data in the Data Lake that are similar to what they had previously in the data warehouse. The difference is that these views are essentially metadata that sits on top of the data in the lake, rather than physically inflexible tables that must be changed by a developer.

Which Approach Should I Choose?

That is a difficult question to answer. If you already have a well-established data warehouse, I don’t recommend tossing everything away and starting again. However, your data warehouse, like many others, may experience some of the challenges I’ve mentioned. If this is the case, you may want to build a Data Lake in addition to your warehouse. The warehouse can continue to function normally, and you can begin adding new data sources to your lake. You may also use it as an archive repository for your warehouse data, which you roll off and maintain available to provide your users access to more data than they’ve ever had before.

You may contemplate shifting your warehouse to the Data Lake as it ages, or you may continue to offer a hybrid strategy.

If you’re just getting started with developing a centralised data platform, I recommend that you look into both options.

What about Technology?

To this point, I have purposefully avoided mentioning any specific technology. The phrase “Data Lake” has come to be associated with big data technologies such as Hadoop, but “data warehouses” are still associated with relational database platforms. The purpose of this article was to emphasise the differences between two data management methodologies rather than a specific technology. The truth remains, however, that the alignment of approaches to the technologies outlined above is not by chance. Because they excel at high-speed searches against tightly structured data, relational database systems are perfect for data warehouse applications.

The Hadoop ecosystem, on the other hand, is ideal for Data Lakes because it easily adapts and scales to very large quantities and can handle any data type or structure. Hadoop, on the other hand, can help with data warehouse applications by adding structured views to raw data. Hadoop excels at providing data and insights to all levels of business users because of its versatility.

What Does the Future Hold?

Both camps’ technologies are still evolving.

Relational database software continues to improve, with advances in both software and hardware targeted at making data warehouses faster, scalable, and dependable.

The Hadoop ecosystem is gaining remarkable traction, and because it is made up of open-source projects that are backed by the community, it is developing and progressing at a much faster rate than traditional software.

Hadoop’s dependence on open-source software and commodity hardware makes it an attractive option to examine from both a cost and feature standpoint if you’re looking for a new data platform or replacing or upgrading an existing system.

Now let us watch this video to gain elaborate insights into Azure Data Lake –

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