The advent of meta data (the appearing of many data i.e. big data) led to the problem that the same information appeared in many places, sometimes with different values making it difficult for decision-makers. Data engineers realized they needed an architectural approach that would allow them to search all these data sources correctly. The key to the answer was the Data Warehousing solution, which finds the correct data for accurate decision-making.
In this blog, you will find:
Choosing the proper Data Warehouse system and using the appropriate data can improve business outcomes through informed decisions, understanding customer behaviour or predicting future trends. In this blog, we will introduce you to data warehouse architecture patterns and the main differences between the three approaches: the Data Warehouse, the Data Lake, and the Data Lakehouse.
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Data Warehousing Architecture Patterns: Three Main Approaches
Choosing the right Data Warehouse architecture depends on organizational requirements, and there are three main approaches: the Data Warehouse, the Data Lake, and the Data Lakehouse. We will guide you through the history, the flow and the benefits and drawbacks of each approach..
Data Warehouses 🏠
A Data Warehouse is a system used for reporting and data analysis. It is considered a core business intelligence component, enabling an organization to consolidate its data into one unified source and making it available for analytics, reporting, and other Business Intelligence (BI) activities. It is designed to provide quick access to the data stored within it so that managers can view and analyze trends across the business.
💡 Background of Data Warehouses
Data warehousing has been around since the 1960s, when the term was created. Over the years, data warehousing has evolved to include more sophisticated architectures and technologies.
• 1960s - Data Warehouse terms, such as Dimensions and Facts, were first developed in data science.
• 1970s – Bill Inmon began to define Data Warehousing and its associated concepts and technologies.
• 1980s – Data Warehouse databases were developed for the first time and continued to evolve
• 1990s– Data Warehousing gained further traction with the publication of books from renowned Data Warehouse pioneers Ralph Kimball and Bill Inmon.
📝 Features of Data Warehouses
Structured data is a type of structured content businesses generate to conduct day-to-day operations. Enterprise Resource Planning (ERP), a timesheet system, or a Customer Relationship Management (CRM) system, have structured data in a Data Warehouse. You can extract data from the Data Warehouse to Perform Business Intelligence (BI) and Structured Query Language (SQL) Analytics.
Think of it like a superstore for data - all your essential information is right there in one place! That is what data warehousing allows us to do: store, manage, and govern our data so you can use it to get business insights for business users.
📣 Benefits and Drawbacks of Data Warehouses
A Data Warehouse is a powerful data storage and analysis tool that can help businesses get the most out of their data. By leveraging Data Warehouses, companies can gain valuable insights into their systems and data, allowing them to make better decisions and improve overall performance. Here are its benefits and drawbacks to consider before you choose the right approach for your business analysis:
• A Data Warehouse allows businesses to store data from multiple sources in a single target data model, which allows for the data to be organized and structured in a meaningful and valuable way for business users.
• A Data Warehouse also maintains data history even if the source systems do not provide a "single source of truth," as data can be restructured and transformed to make sense of it.
• A Data Warehouse is ideal for Business Intelligence (BI) and Analytics, as data can be stored in an organized manner, making data retrieval more accessible and more efficient.
• With a Data Warehouse, businesses can improve query performance, reduce data redundancy, and increase data accuracy.
• The most significant issue is their difficulty dealing with semi-structured and unstructured data
• Also, creating ETL/ELT pipelines (data integration methods) to integrate data from different sources can be long and complicated.
• The "single source of truth" is also hard to achieve due to businesses' constantly changing processes, systems, and requirements.
• Data Warehouses are not ideal for Machine Learning (ML) applications since data must be pre-aggregated for Data Warehouses to offer any significant performance gains.
Data Lake 🏞️
A Data Lake is like a Data Warehouse, but the data is not organized or structured in any way. A Data Lake stores actual data in its raw form and allows for data analysis on a much larger scale than traditional Data Warehouses. It also enables users to store substantial amounts of data from multiple sources and access it quickly as needed. With Data Lakes, data sources can be stored and processed in their native format, allowing for more flexibility and scalability.
💡 Background of Data Lake
A Data Lake is a data storage and management system that allows data to be stored in its native format.
• 2011 – Data Lake was first coinedas a specialized Data Warehouse designed to store structured, semi-structured, and unstructured data.
• 2016 – Data Lakes became available for all major cloud vendors and have since become a popular data management tool for businesses of all sizes.
• 2018 – As Data Lakes became overloaded with data from various sources, the term data swamp was first coined to describe Data Lakes that are poorly managed and contain data of questionable quality.
📝 Features of Data Lake
The Data Lake allows data from all sources, including structured, textual, and other unstructured data, to be stored in one place. This data source can then be accessed using big data tools and technologies.
The Data Lake permits data to be stored in its raw form, which makes it easier to search and analyze data from multiple sources. Furthermore, Data Lakes allow data to be accessed with open standards rather than proprietary formats used by Data Warehouses. This allows data to be easily shared across different analytics engines and platforms, such as machine learning systems that can help uncover valuable data insights.
📣 Benefits and Drawbacks of Data Lake
Data Lakes are seen as an alternative to Data Warehouses which often require expensive data modelling and governance processes. By eliminating these barriers, Data Lakes provide a more efficient way of managing data and allow organizations to get insights more quickly.
• Data Warehouses provide huge data processing at a low cost.
• Data Lakes offer a rapid data ingestion rate and can handle any data type - structured, semi-structured or unstructured.
• Data Lakes can break down data silos and are ideal for Machine Learning applications.
• Data Lakes can become disorganized over time, resulting in a so-called data swamp.
• In addition, data stored in Data Lakes are usually slower to query from than data stored in Data Warehouses, and Data Lakes are not ideal for traditional business intelligence (BI) and analytics.
Data Lakehouse 🏞️🏠
A Data Lakehouse is a data management platform that combines the capabilities of a Data Warehouse and a Data Lake. It provides advanced data integration, governance, and analytics features to improve decision-making for organizations.
💡 Background of Data Lakehouse
• 2017 – The Data Lakehouse term was first coined to bridge the gap between traditional Data Warehouses, which store structured data, and Data Lakes, which store unstructured data.
• 2019 – The Delta Lake project beganaiming to provide data reliability and data governance for Data Lakes.
• 2020 – The Iceberg project became a top-level project, providing data management capabilities.
📝 Features of Data Lake
A Data Lakehouse is an advanced data management system that combines data from various sources into one data repository. All data, whether structured, textual, or other unstructured data, is stored in open file formats and can be used for BI and SQL analytics, real-time applications, data science and machine learning. A Data Lakehouse provides a secure storage, access, and processing environment.
📣 Benefits and Drawbacks of Data Lake
• A Data Lakehouse helps organizations meet their data needs for Business Intelligence (BI), analytics, and machine learning without duplicating data sets or creating multiple data copies.
• With a Data Lakehouse, data teams can easily and quickly query data from one source, reducing the time it takes to access data.
• This system also provides better data governance and security features to ensure data integrity and trustworthiness.
• Additionally, Data Lakehouses allow organizations to quickly deploy new analytics applications or machine learning models in production.
• Data Lakehouse technology is still relatively new, and it may not be able to provide an all-encompassing view of the data available.
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Content from: ProServeIT Academy 2022 Microsoft Data Analytics Course 2 by Scott Sugar
Edited by: Betty Quon & Hyun-Jin Im