If you are looking for the best solution for your company, you may the use of Data Lake and Data Warehouse be exactly what you're looking for, because it allows you to make better informed decisions thanks to its data analysis and storage technology. As you well know, analytics has become critical for businesses to stay competitive, monitor business performance, and support the best possible path.
The data management provided by this technology allows you to create these reports, dashboards, and analysis tools. Storing data efficiently to reduce data I/O to quickly deliver query results to hundreds and thousands of users simultaneously should be a priority.
The architecture of this type of technology is made up of layers, each of which is equally important for the best results. In the first one is the frontend client that presents the results through reports, analysis and data mining tools. The next one positions the analysis engine that is used to access the data and to analyze it. In the last one is the database server, where the data is loaded and stored.
Data is stored in two different ways: 1) frequently accessed data is stored on very fast storage (such as SSD drives); and, 2) infrequently accessed data is stored in cost-effective object storage, such as Amazon S3. To perfect each of the layers, you can make a query to help you reduce the greatest number of losses and problems that may arise.
If you are interested in implementing the use of Data Warehouse and Data Lake in your company to optimize your results, at Codster we can help you understand these technologies and how they can be useful to maintain the competitiveness that you need.
What is a Data Lake and a Data Warehouse?
A Data Warehouse is a central repository of all the information that can be analyzed to make better informed decisions.. In this way, data in your business can flow into a data warehouse from transactional systems, relational databases, and other sources, typically at a regular cadence no matter where you are in the world.
Some of the benefits of implementing a Data Warehouse include the following examples:
- Informed decision making
- Consolidated data from many sources
- Historical data analysis
- Data quality, consistency and accuracy
- Separation of analysis processing from transactional databases, which improves the performance of both systems
That said, this type of data warehouse can contain several databases of different types and origins, organize them, and thus facilitate their access. Within each database, the data is organized in tables and columns. In turn, in each column, you can define a description of the data for classification, such as an integer, a data field or a string. In turn, the tables can be organized within schemes, which can be conceived as folders for their hierarchy. When data is entered, it is stored in various tables described by the schema.
Query tools use the schema to determine which data tables to access and which to parse. This process may sound delicate and slow, but thanks to the use of Data Lakes, it can be easily solved.
A Data Lake is a centralized repository designed to store, process, and protect large amounts of structured, semi-structured, or unstructured data. You can store data in its native format and process any variety of data, ignoring size limits.
A data lake provides a scalable and secure platform that enables businesses to: transfer any data from any system, at any speed (even if the data comes from systems that are on-premises, cloud, or edge processing); store any type or volume of data with absolute fidelity; process data in real time or in batch mode; and analyze data using SQL, Python, R or any other language, third party data or statistics applications.
Data Lakes were designed with the main objective of controlling large volumes of big data, so companies can move raw data via batch or stream to a data lake without having to transform it for analysis or use. Companies rely on this technology in key situations to achieve the following main objectives:
- Lower Total Cost of Ownership
- Simplify data management
- Prepare to incorporate artificial intelligence and machine learning
- Speed up the generation of statistics
- Improve security and control
The synergy between Data Lake and Data Warehouse
Business analysts, data engineers, data scientists, and decision makers access data using business intelligence (BI) tools, SQL clients, and other analytics applications. As the volume and variety of data increases, it is helpful to follow one or more known patterns for working with data in the database, data lake, and data warehouse.
While data lakes and data warehouses store data to some extent, each is optimized for different uses. They serve as complementary tools to each other, rather than two antagonistic options. Also, in some cases, companies may need to use both for best results. As a point of comparison, Warehouses are often ideal for the type of repeatable reporting and analysis that is common in business practices, such as monthly sales reports, tracking sales by region, or website traffic.
A data lake is different in that it stores relational data from line-of-business applications and non-relational data from mobile applications, IoT devices, and social media. The data structure or schema is not defined when the data is captured. This means that you can store all your data without careful design and without knowing what questions you might have to answer in the future.
To correctly implement this information, different types of analytics can be used, such as SQL queries, big data analytics, full-text search, real-time analytics, and machine learning.
As organizations with data warehouses recognize the benefits of data lakes, enable various query capabilities, data science use cases, and advanced capabilities to discover new information models, they translate their benefits into “data management solutions.” For analysis".
Benefits of its use
When you try to determine if your company needs a Data Lake or Data Warehouse, please note the following: the types of data you work with, what you want to do with the data, the complexity of the data collection process, and your data governance and management strategy, as well as the tools and skill set your organization has . That said, it's important to note that organizations that successfully create business value from their successful use of data will, in fact, outperform their peers.
A survey conducted by the Aberdeen team noted that organizations that implemented Data Lakes outperformed peers by 9 1TP2Q in organic revenue growth. This result is mainly due to the fact that the leaders of these companies were able to carry out new and better types of analysis, such as the use of machine learning on new origins, such as log files, clickstream data, social networks, and Internet-connected devices stored in data lakes.
This helped them identify business growth opportunities faster and act on them by attracting and retaining customers, boosting productivity, proactively maintaining devices and making informed decisions.
In addition to the above, companies are beginning to consider the value of data lake implementation and Data Warehouse from another perspective: a data lake not only serves to store data with absolute fidelity, it allows users to gain a deeper understanding of business situations, as they have more context than ever before, allowing users to speed up stats experiments. For its part, a Data Warehouse allows you to generate more detailed and useful reports and reports.
Data Lake and Data Warehouse through Cloud Services
AWS can help you implement Data Lake and Data Warehouse through cloud technology, this way, you won't have to worry about the physical architecture you require. It will be even easier to analyze the entire data lake, especially that information coming from IoT devices, with a variety of analytical strategies that include machine learning. As a result, more organizations run their data and analytics lakes on AWS than anywhere else, with a variety of customers, including NETFLIX, Zillow, NASDAQ, Yelp, iRobot, and FINRA, relying on AWS to run their workloads. fundamental analysis for their companies.
Regarding the Data Warehouse, cloud technology allows you to take advantage of the main benefits associated with computing on demand- Seamless access to virtually unlimited storage and compute capacity, the ability to scale your system based on the growth of data collected, stored, and queried, and pay only for the resources you provision. That said, it also offers a rich set of managed services that seamlessly integrate with each other, so you can quickly implement a comprehensive data warehousing and analytics solution.
Its correct implementation can help different industries, for example, a company that offers streaming music, radio and podcasts. you can increase your income if you improve your recommendation system through data analysis, so that users consume more of its service, which would allow the company to sell more ads. A multinational telecommunications company can save money by compiling churn models that reduce customer churn. Or an investment company can use data lakes to fuel machine learning so that it can manage portfolio risks as soon as real-time market data is accessible.
Before making a decision about the use of Data Lake and Data Warehouse to implement good data analysis in your company, it is necessary to analyze in detail the benefits and advantages that it can offer, as well as the challenges that it would represent for your team. If you are interested learn more about this, discover the solutions it offers and will offer Codster through a consultancy that can help solve all your doubts.