3 items tagged "data lakes"

  • Data Lakes and the Need for Data Version Control  

    Data Lakes and the Need for Data Version Control

    In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident. 

    In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management.

    Understanding Data Lakes

    A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition. As a result, data lakes can accommodate vast volumes of data from different sources, providing a cost-effective and scalable solution for handling big data.

    Before we address the questions, ‘What is data version control?’ and ‘Why is it important for data lakes?’, we will discuss the key characteristics of data lakes.

    Schema-on-Read vs. Schema-on-Write

    Data lakes follow the ‘Schema-on-Read’ approach, which means data is stored in its raw form, and schemas are applied at the time of data consumption. In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database.

    Flexibility and Agility

    Data lakes provide flexibility, enabling organizations to store diverse data types without worrying about immediate data modeling. This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure.

    Cost-Efficiency

    By leveraging cost-effective storage solutions like the Hadoop Distributed File System (HDFS) or cloud-based storage, data lakes can handle large-scale data without incurring prohibitive costs. This is particularly advantageous when dealing with exponentially growing data volumes.

    Data Lakes vs. Data Warehouses and Relational Databases

    It is essential to distinguish data lakes from data warehouses and relational databases, as each serves different purposes and has distinct characteristics.

    Data Warehouses

    Some key characteristics of data warehouses are as follows:

    • Data Type: Data warehouses primarily store structured data that has undergone ETL (Extract, Transform, Load) processing to conform to a specific schema.
    • Schema Enforcement: Data warehouses use a “schema-on-write” approach. Data must be transformed and structured before loading, ensuring data consistency and quality.
    • Processing: Data warehouses employ massively parallel processing (MPP) for quick query performance. They are optimized for complex analytical queries and reporting.
    • Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression.
    • Use Cases: Data warehouses are tailored for business analysts, decision-makers, and executives who require fast, reliable access to structured data for reporting, business intelligence, and strategic decision-making.

    In summary, data lakes prioritize data variety and exploration, making them suitable for scenarios where the data landscape is evolving rapidly, and the initial data structure might not be well-defined also data lakes are more suitable for storing diverse and raw data for exploratory analysis, while data warehouses focus on structured data, ensuring data quality and enabling efficient querying for business-critical operations like business intelligence and reporting.

    Relational Databases

    Some key characteristics of relational databases are as follows:

    • Data Structure: Relational databases store structured data in rows and columns, where data types and relationships are defined by a schema before data is inserted.
    • Schema Enforcement: Relational databases use a “schema-on-write” approach, where data must adhere to a predefined schema before it can be inserted. This ensures data consistency and integrity.
    • Processing: Relational databases are optimized for transactional processing and structured queries using SQL. They excel at managing structured data and supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions.
    • Scalability: Relational databases can scale vertically by upgrading hardware, but horizontal scaling can be more challenging due to the need to maintain data integrity and relationships.
    • Use Cases: Relational databases are commonly used for applications requiring structured data management, such as customer relationship management (CRM), enterprise resource planning (ERP), and online transaction processing (OLTP) systems.

    Data lakes are designed for storing and processing diverse and raw data, making them suitable for exploratory analysis and big data processing. Relational databases are optimized for structured data with well-defined schemas, making them suitable for transactional applications and structured querying.

    The Importance of Data Version Control in Data Lakes

    As data lakes become the backbone of modern data infrastructures, the management of data changes and version control becomes a critical challenge. Data version control refers to the ability to track, manage, and audit changes made to datasets over time. This is particularly vital in data lakes for the following reasons.

    Data Volume and Diversity

    Data lakes often contain vast and diverse datasets from various sources, with updates and additions occurring continuously. Managing these changes efficiently is crucial for maintaining data consistency and accuracy.

    Collaborative Data Exploration

    In data lakes, multiple teams and stakeholders collaboratively explore data to derive insights. Without proper version control, different users may inadvertently overwrite or modify data, leading to potential data integrity issues and confusion.

    Auditing and Compliance

    In regulated industries or environments with strict data governance requirements, data version control is essential for tracking changes, understanding data lineage, and ensuring compliance with regulations.

    Handling Changes at Scale with Data Version Control

    To effectively handle changes at scale in data lakes, robust data version control mechanisms must be implemented. Here are some essential strategies:

    • Time-Stamped Snapshots: Maintaining time-stamped snapshots of the data allows for a historical view of changes made over time. These snapshots can be used to roll back to a previous state or track data lineage.
    • Metadata Management: Tracking metadata, such as data schema, data sources, and data transformation processes, aids in understanding the evolution of datasets and the context of changes.
    • Access Controls and Permissions: Implementing fine-grained access controls and permissions ensures that only authorized users can make changes to specific datasets, reducing the risk of unauthorized modifications.
    • Change Tracking and Notifications: Setting up change tracking mechanisms and notifications alerts stakeholders about data modifications, ensuring transparency and awareness.
    • Automated Testing and Validation: Automated testing and validation procedures help detect and rectify any anomalies or inconsistencies resulting from data changes.

    Conclusion

    Data lakes have revolutionized the way organizations manage and analyze large-scale data. Their ability to store diverse data types without predefined schemas makes them highly flexible and cost-efficient. However, managing changes in data lakes requires careful attention to ensure data consistency, accuracy, and compliance. 

    Data version control plays a crucial role in addressing these challenges, enabling organizations to handle changes at scale and derive valuable insights from their data lakes with confidence and reliability. By implementing robust version control mechanisms and following best practices, businesses can leverage data lakes to their full potential, driving innovation and informed decision-making.

    Date: September 21, 2023

    Author: Kruti Chapaneri

    Source: ODSC

     

  • 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.

    Purpose

    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.

    Users

    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.

    Tasks

    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.

    Size

    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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