One of the great technological challenges that companies have to face is, undoubtedly, data growth. Who hasn't heard of terabytes, petabytes y exabytes? Today they are common terms in almost all sectors, especially when it comes to storage capacity.
One thing is clear: The emergence of new technologies on the Internet has resulted in excessive access and storage of information from both customers themselves and potential customers. Y, given the large amount of data, it is essential to have a system that keeps them safe, like Data Lake.
What is a data lake?
In accordance with Amazon web services the definition of Data Lake is:
Centralized repository that makes it possible to store all structured and unstructured data at any scale. You can store your data as is, without having to structure them first, and run different types of analysis, from dashboards and visualizations to big data processing, real-time analytics and machine learning to make better decisions.
The term Data Lake (literally, data lake in English) was coined by James Dixon, Pentaho Chief Technology Officer, and refers to the particular nature of the data in this system, in contrast to clean and processed data stored in traditional data storage systems or Data Mart.
According to Dixon, “If you think of a Data Mart as a clean bottled water warehouse, packaged and structured for easy consumption, a Data Lake would be a large body of water in a more natural state. Its content comes from a source that fills the lake and several users of the same can approach to examine, dive or take samples ".
Los Data Lakes de forma general se configuran en un clusterA cluster is a set of interconnected companies and organizations that operate in the same sector or geographical area, and that collaborate to improve their competitiveness. These groupings allow for the sharing of resources, Knowledge and technologies, fostering innovation and economic growth. Clusters can span a variety of industries, from technology to agriculture, and are fundamental for regional development and job creation.... de hardware de consumo escalable y económico, allowing data to be dumped into it in case it is needed later without having to worry about storage capacity. These clusters can exist locally or in the cloud.
Why use a data lake
According to the study Searching for knowledge in today's data lake And Aberdeen, Institutions that successfully generate business value from their data will outperform their competitors. In reality, companies that implemented a data lake outperformed their peers by a 9% in organic revenue growth.
Therefore, were able to perform new types of analysis, like machine learning, in new sources, as log files, clickstream data, social media and internet-connected devices stored in a data lake.
This helped them identify and act on business growth possibilities more quickly., attracting and retaining customers, boosting productivity, proactively maintaining devices and making informed decisions.
5 advantages of a data lake
Among the main benefits of a Data Lake are the following:
- It makes it possible to centralize all the data in one place, whatever its origin. Once included in their respective information silo, can be processed with Big Data tools. It is possible that in the face of such a disparity in information there are data that need special treatment with respect to security., but it is a solvable aspect with this system.
- The original source of the data may be out of date or disabled, but its content can still be valuable for analysis. With this system you can enter this information.
- All data that reaches the system can be normalized and enriched.
- The data is prepared according to the needs of the moment, which significantly reduces costs and times.
- Any authorized user can enter and enrich the information from anywhere, helping the organization to more easily collect the data needed to make decisions.
Data Lake frente a Data Warehouse
When talking about data storage, another concept related to the topic at hand usually arises.: el Data Warehouse o data warehouse. Se trata de una databaseA database is an organized set of information that allows you to store, Manage and retrieve data efficiently. Used in various applications, from enterprise systems to online platforms, Databases can be relational or non-relational. Proper design is critical to optimizing performance and ensuring information integrity, thus facilitating informed decision-making in different contexts.... optimizada para analizar datos relacionales de sistemas transaccionales y aplicaciones de línea de negocio.
Despite this, even though both paradigms focus on data storage, there are some differences between a data lake and a data warehouse:
- Data structure: a data warehouse only collects structured data, while a data lake collects structured and unstructured data.
- Purpose of the data: this aspect may or may not be defined in a Data Lake, mientras que en un Data Warehouse no hay marginMargin is a term used in a variety of contexts, such as accounting, Economics and printing. In accounting, refers to the difference between revenue and costs, which allows the profitability of a business to be evaluated. In the publishing field, The margin is the white space around the text on a page, that makes it easy to read and provides an aesthetic presentation. Its correct management is essential.. para la improvisación.
- Flexibility: in a Data Lake it is easier to make changes because it has no structure, but in a Data Warehouse it is more complex because other processes intervene.
- Scheme: data lakes focus on schemas on read and data stores on schemas on write.
- Users: in a Data Lake the data is managed by analysts, while in a Data Warehouse any user with access can manage the data.
- Accessibility: while in a Data Lake there is great and easy accessibility, in a Data Warehouse this section is more expensive and complex.
- Storage: a Data Lake has a limited cost with an opportunity for expansion in the cloud, while a Data Warehouse is generally more expensive.
As a last resort, both systems are intended for institutions that base their decisions on data and that can implement more personalized or customer-centric strategies and communications.
Azure data lake
Azure data lake is Microsoft's hyperscale repository for large cloud data analytics workloads. This service is designed for the cloud, es compatible con HDFSHDFS, o Hadoop Distributed File System, It is a key infrastructure for storing large volumes of data. Designed to run on common hardware, HDFS enables data distribution across multiple nodes, ensuring high availability and fault tolerance. Its architecture is based on a master-slave model, where a master node manages the system and slave nodes store the data, facilitating the efficient processing of information.. (Hadoop Distributed File SystemThe Hadoop Distributed File System (HDFS) is a critical part of the Hadoop ecosystem, Designed to store large volumes of data in a distributed manner. HDFS enables scalable storage and efficient data management, splitting files into blocks that are replicated across different nodes. This ensures availability and resilience to failures, facilitating the processing of big data in big data environments....) and scales without limits with massive performance and enterprise-grade capabilities.
Azure Data Lake solves many of the productivity and scalability challenges that prevent institutions from maximizing the value of data resources with a service that is ready to meet their current and future business needs..
Among the different services included in Azure Data Lake are the following:
- Data Lake Analytics: Unlimited cloud analytics job service that enables you to develop and run parallel data transformation and processing programs using U-SQL languages, R, Python y .Net.
- HDInsight: Cloud ServiceThe "Cloud Service" refers to the delivery of computing resources over the Internet, allowing users to access storage, processing and applications without the need for on-premises physical infrastructure. This model offers flexibility, Scalability and cost savings, since companies only pay for what they use. What's more, Facilitates collaboration and data access from anywhere, improving operational efficiency in various industries.. of Apache SparkApache Spark is an open-source data processing engine that enables the analysis of large volumes of information quickly and efficiently. Its design is based on memory, which optimizes performance compared to other batch processing tools. Spark is widely used in big data applications, Machine Learning and Real-Time Analytics, thanks to its ease of use and... y Hadoop para compañías que proporciona clústeres de análisis open source para Spark, HiveHive is a decentralized social media platform that allows its users to share content and connect with others without the intervention of a central authority. Uses blockchain technology to ensure data security and ownership. Unlike other social networks, Hive allows users to monetize their content through crypto rewards, which encourages the creation and active exchange of information...., Map Reduce, HBaseHBase is a NoSQL database designed to handle large volumes of data distributed in clusters. Based on the column model, Enables fast, scalable access to information. HBase easily integrates with Hadoop, making it a popular choice for applications that require massive data storage and processing. Its flexibility and ability to grow make it ideal for big data projects...., Storm, Kafka y R-Server, backed by a service level agreement from the 99,9%.
- Data Lake Store: Unlimited cloud data repository for big data analytics that can be massively scaled and built based on the open HDFS standard.
