LAKEHOUSE AZURE SERIAL

Data is shared in a standard way that is understandable for all the users,.SQL language, which is used to query structures.Therefore, organizations often use specialized tools for advanced analytics, although these tools can connect to traditional databases as a source, it is not an ideal environment for them.ĭespite numerous problems, data warehouses have many advantages, such as: They can handle large data sets, but they are not optimized for advanced analytics such as machine learning and AI. This is where big data tools come into play.

LAKEHOUSE AZURE SERIAL

Traditional databases with parallel or serial processing excel at handling structured data, but they struggle with large volume of data and they are not well-suited for handling unstructured forms. This includes structured data from relational databases, semi-structured data from website activities, and unstructured data such as images and videos. Data warehouses ensured a single source of truth, meaning that all data required for analysis is derived from a unified source, ensuring consistency and accuracy in results when the same calculations are performed.ĭata is generated from a variety of sources on a large scale today. What was and still is relevant from the organizations’ point of view is the approach used during implementation of this type of solutions. An example of this is Dedicated SQL Pool available in Azure Synapse Analytics ecosystem. These types of technologies are still used on the market and play a crucial role in modern data projects.

At that time, technologies such as MPP (Massive Parallel Processing) appeared, which engaged clusters to process data portions in parallel, resulting in much more efficient methods of data analysis. As a result, specialized databases were established to store pre-aggregated and processed data that is optimized for analytical requirements. Due to the high level of query complexity and poor performance, the vast amounts of data at that time made it impossible to directly select data for analytical purposes from operational databases. This approach is not new and dates back to the 1980s. Of course, I am talking about lakehouse, a kind of merge between two different areas: a traditional data warehouse and a data lake from the world of big data processing.īefore we proceed to describe the main topic, let’s say a few words about data warehouses themselves. In this article, a concept will be presented that has become extremely popular and is being implemented more often in various types of organizations. Some of these concepts disappeared faster than others, while others have become standards and have been implemented for years in many organizations around the globe. For those interested in the topic, concepts such as data lake, data warehouse, or data mesh are likely familiar.

Over the years, many different concepts related to systems dedicated to data analysis have appeared on the market.

Lakehouse azure