2 items tagged "data lakes"

  • The differences between data lakes and data warehouses: a brief explanation

    The differences between data lakes and data warehouses: a brief explanation

    When comparing data lake vs. data warehouse, it's important to know that these two things actually serve quite different roles. They manage data differently and serve their own types of functions.

    The market for data warehouses is booming. One study forecasts that the market will be worth $23.8 billion by 2030. Demand is growing at an annual pace of 29%.

    While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. 

    Both data warehouses and data lakes are used when storing big data. On the other hand, they are not the same. A data warehouse is a storage area for filtered, structured data that has been processed already for a particular use, while Data Lake is a massive pool of raw data and the aim is still unknown.

    Many people are confused about these two, but the only similarity between them is the high-level principle of data storing.  It is vital to know the difference between the two as they serve different principles and need diverse sets of eyes to be adequately optimized. However, a data lake functions for one specific company, the data warehouse, on the other hand, is fitted for another.

    This blog will reveal or show the difference between the data warehouse and the data lake. Below are their notable differences.

    Data Lake

    • Type of Data: structured and unstructured from different sources of data
    • Purpose: Cost-efficient big data storage
    • Users: Engineers and scientists
    • Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning
    • Sizes: Store data which might be utilized

    Data Warehouse

    • Data Type: Historical which has been structured in order to suit the relational database diagram
    • Purpose: Business decision analytics
    • Users: Business analysts and data analysts
    • Tasks: Read-only queries for summarizing and aggregating data
    • Size: Just stores data pertinent to the analysis

    Data Type

    Data cleaning is a vital data skill as data comes in imperfect and messy types. Raw data that has not been cleared is known as unstructured data; this includes chat logs, pictures, and PDF files. Unstructured data that has been cleared to suit a plan, sort out into tables, and defined by relationships and types, is known as structured data. This is a vital disparity between data warehouses and data lakes.

    Data warehouses contain historical information that has been cleared to suit a relational plan. On the other hand, data lakes store from an extensive array of sources like real-time social media streams, Internet of Things devices, web app transactions, and user data. This data is often structured, but most of the time, it is messy as it is being ingested from the data source.


    When it comes to principles and functions, Data Lake is utilized for cost-efficient storage of significant amounts of data from various sources. Letting data of whichever structure decreases cost as it is flexible as well as scalable and does not have to suit a particular plan or program. On the other hand, it is easy to analyze structured data as it is cleaner. It also has the same plan to query from. A data warehouse is very useful for historical data examination for particular data decisions by limiting data to a plan or program.

    You might see that both set off each other when it comes to the workflow of the data. The ingested organization will be stored right away into Data Lake. Once a particular organization concern arises, a part of the data considered relevant is taken out from the lake, cleared as well as exported.


    Each one has different applications, but both are very valuable for diverse users. Business analysts and data analysts out there often work in a data warehouse that has openly and plainly relevant data which has been processed for the job. Data warehouse needs a lower level of knowledge or skill in data science and programming to use.

    Engineers set up and maintained data lakes, and they include them into the data pipeline. Data scientists also work closely with data lakes because they have information on a broader as well as current scope.


    Engineers make use of data lakes in storing incoming data. On the other hand, data lakes are not just restricted to storage. Keep in mind that unstructured data is scalable and flexible, which is better and ideal for data analytics. A big data analytic can work on data lakes with the use of Apache Spark as well as Hadoop. This is true when it comes to deep learning that needs scalability in the growing number of training information.

    Usually, data warehouses are set to read-only for users, most especially those who are first and foremost reading as well as collective data for insights. The fact that information or data is already clean as well as archival, usually there is no need to update or even insert data.


    When it comes to size, Data Lake is much bigger than a data warehouse. This is because of the fact that Data Lake keeps hold of all information that may be pertinent to a business or organization. Frequently, data lakes are petabytes, which is 1,000 terabytes. On the other hand, the data warehouse is more selective or choosy on what information is stored.

    Understand the Significance of Data Warehouses and Data Lakes

    If you are settling between data warehouse or data lake, you need to review the categories mentioned above to determine one that will meet your needs and fit your case. In case you are interested in a thorough dive into the disparities or knowing how to make data warehouses, you can partake in some lessons offered online.

    Always keep in mind that sometimes you want a combination of these two storage solutions, most especially if developing data pipelines.

    Author: Liraz Postan

    Source: Smart Data Collective

  • Why data lakes are the future of data storage

    Why data lakes are the future of data storage

    The term big data has been around since 2005, but what does it actually mean? Exactly how big is big? We are creating data every second. It’s generated across all industries and by a myriad of devices, from computers to industrial sensors to weather balloons and countless other sources. According to a recent study conducted by Data Never Sleeps, there are a quintillion bytes of data generated each minute, and the forecast is that our data will only keep growing at an unprecedented rate.

    We have also come to realize just how important data really is. Some liken its value to something as precious to our existence as water or oil, although those aren’t really valid comparisons. Water supplies can fall and petroleum stores can be depleted, but data isn’t going anywhere. It only continues to grow. Not just in volume, but also in variety and velocity. Thankfully, over the past decade, data storage has become cheaper, faster and more easily available, and as a result, where to store all this information isn’t the biggest concern anymore. Industries that work in the IoT and faster payments space are now starting to push data through at a very high speed and that data is constantly changing shape.

    In essence, all this gives rise to a 'data demon'. Our data has become so complex that normal techniques for harnessing it often fail, keeping us from realizing data’s full potential.

    Most organizations currently treat data as a cost center. Each time a data project is spun off, there is an 'expense' attached to it. It’s contradictive. On the one side, we’re proclaiming that data is our most valuable asset, but on the other side we perceive it as a liability. It’s time to change that perception, especially when it comes to banks. The volumes of data financial institutions have can be used to create tremendous value. Note that I’m not talking about 'selling the data', but leveraging it more effectively to provide crisp analytics that deliver knowledge and drive better business decisions.

    What’s stopping people from converting data from an expense to an asset, then? The technology and talent exist, but the thought process is lacking.

    Data warehouses have been around for a long time and traditionally were the only way to store large amounts of data that’s used for analytical and reporting purposes. However, a warehouse, as the name suggests, immediately makes one think of a rigid structure that’s limited. In a physical warehouse, you can store products in three dimensions: length, breadth and height. These dimensions, though, are limited by your warehouse’s architecture. If you want to add more products, you must go through a massive upgrade process. Technically, it’s doable, but not ideal. Similarly, data warehouses present a bit of rigidity when handling constantly changing data elements.

    Data lakes are a modern take on big data. When you think of a lake, you cannot define its shape and size, nor can you define what lives in it and how. Lakes just form, even if they are man-made. There is still an element of randomness to them and it’s this randomness that helps us in situations where the future is, well, sort of unpredictable. Lakes expand and contract, they change over periods of time, and they have an ecosystem that’s home to various types of animals and organisms. This lake can be a source of food (such as fish) or fresh water and can even be the locale for water-based adventures. Similarly, a data lake contains a vast body of data and is able to handle that data’s volume, velocity and variety.

    When the mammoth data organizations like Yahoo, Google, Facebook and LinkedIn started to realize that their data and data usage were drastically different and that it was almost impossible to use traditional methods to analyze it, they had to innovate. This in turn gave rise to technologies like document-based databases and big data engines like Hadoop, Spark, HPCC Systems and others. These technologies were designed to allow the flexibility one needs when handling unpredictable data inputs.

    Jeff Lewis is SVP of Payments at Sutton Bank, a small community bank that’s challenging the status quo for other banks in the payments space. 'Banks have to learn to move on from data warehouses to data lakes. The speed, accuracy and flexibility of information coming out of a data lake is crucial to the increased operational efficiency of employees and to provide a better regulatory oversight', said Lewis. 'Bankers are no longer old school and are ready to innovate with the FinTechs of the world. A data centric thought process and approach is crucial for success'.

    Data lakes are a natural choice to handle the complexity of such data, and the application of machine learning and AI are also becoming more common, as well. From using AI to clean and augment incoming data, to running complex algorithms to correlate different sources of information to detect complex fraud, there is an algorithm for just about everything. And now, with the help of distributed processing, these algorithms can be run on multiple clusters and the workload can be spread across nodes.

    One thing to remember is that you should be building a data lake and not a data swamp. It’s hard to control a swamp. You cannot drink from it, nor can you navigate it easily. So, when you look at creating a data lake, think about what the ecosystem looks like and who your consumers are. Then, embark on a journey to build a lake on your own.

    Source: Insidebigdata

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