Data Warehouses and Data Lakes are two different varieties of data storage repositories. Organizations utilise these to store, manage and analyze data. Data warehouses have been around for a long time as the industry used it to save structured data, clean up and arrange for particular market approaches, and pass it to reporting or BI tools. On the contrary, Data Lake is a newer technology. It earned popularity by Hadoop and its open source ecosystem. A data lake allows storing data in its original form, and processing later during an analysis only. The two technologies have differences as well as it goes hand in hand, particularly as many progress to cloud-native data infrastructure. We’ll explain and make you understand the difference between Data Warehouse and Data Lake.
What is a Data Lake?
A Data Lake is a repository or a storage location of data collected in its original format, normally object blobs or files. A data lake is customarily a single store of all business data comprising raw copies of source system data and modified data used for assignments such as reporting, visualization, analytics and machine learning. A data lake is a very scalable storage system that accommodates structured and unstructured data. A data lake does not need planning or former knowledge of the data analysis. It implies that analysis will follow later when required. One example of technology utilised to host a data lake is the categorised file system used in Apache Hadoop. Many corporations use cloud storage services for instance, Azure Data Lake and Amazon S3.
What is a Data Warehouse?
Data warehouse solutions are created to contain abstracted data from many applications and data sources, normally formed by business function. Standard data sources are Online Transaction Processing (OLTP) databases that store transaction data, customer relationship management (CRM), and Enterprise Resources Planning (ERP).
Traditional data warehouses use a method termed Extract Transform Load (ETL). Data is meticulously outlined from the original data sources to tables in the data warehouse and go through transformations to accomplish a structured format, to allow reporting and BI analysis.
There are many varieties of data warehouses, including Enterprise Data Warehouse (EDW) which produces decision support for a whole organization, an Operational Data Store (ODS), employed for routine tasks like transaction recording or employee data reporting, and Data Marts, smaller data warehouses for particular business functions.
Major Differences Between Data Warehouse and Data Lake
Data lakes and data warehouses are both extensively used for storing big data, but they are not replaceable names. When data lake is an enormous pool of raw data, the idea for which is not yet determined; the data warehouse is a repository for well-structured, refined data that has previously been processed for a particular target.
These two data storages are often-times confused but are actually more different than they are similar. In fact, the only real similarity between them is the high-level design of storing data. The characteristics are significant because they show the differences and need sets of sights to be correctly optimized. When a data lake works for a company, a data warehouse may suit another one.
Data warehouses store structured organizational data. For instance, financial transactions, CRM and ERP data. Other data sources like social media, web server logs, and sensor data, as well as documents and rich media, are not stored since they are more complicated to model, and their sheer volume causes them to be expensive and challenging to manage. These sorts of data are analysed more suitable for a data lake.
Data is organized and defined in a data warehouse. Moreover, metadata is implemented before the data is written and stored. This method is named as ‘schema on write’.
A data lake applies everything, inclusive of the data models recognised unsuitable for a data warehouse. Data is stored in a raw manner; information is gathered to the schema as data is removed from the data source, not when written to storage. This is distinguished as a ‘schema on read’.
Storage and Data Retention
Before data are being loaded to a data warehouse, data engineers investigate them thoroughly and also examine how to apply them for business analysis. They create transformations to review and transform the data to enable the extraction of important insights. Data that doesn’t respond to concrete business questions are not incorporated in the data warehouse. In order to decrease storage space and enhance performance, a traditional data warehouse is an overpriced and inadequate enterprise resource.
In a data lake, data retention is less complex, as it preserves all data that is raw, structured, and unstructured. Data is never removed, allowing analysis of past, current and future information. Data lakes can simply be designed and scaled to Petabytes. They work on commodity servers using inexpensive storage devices, removing storage limitations.
Data warehouses collect historical data. Incoming data adheres to a predefined arrangement. This is helpful for solving particular business questions such as revenue and profitability across many outlets in a duration of past time.
But, if marketing questions are growing to be complex, or the business wants to preserve all data to allow in-depth analysis, data warehouses are inadequate. The expansion work to adapt the data warehouse and ETL process to new business questions is a large responsibility.
A data lake saves data in its original format, so it is instantly approachable for any type of investigation. Information can be recovered and reused. A user can employ a formalized schema to the data, store it, and share it among others. If the information is not beneficial, the copy can be dismissed without changing the data stored in the data lake. All this is achieved without development effort.
Data warehouses have seen the industry for pretty some time and are a reliable, performance-fit technology. Data lakes are approaching to that stage, but are newer and have a less enterprise track record. A large company cannot acquire and execute a data lake as it would with data warehouses, because it must recognise which means to use, open source or commercial, and how to piece them together to meet demands, etc.
The users of the Data Warehouse and Data Lake may vary. Data Scientists or Business Professionals use either these according to their requirement. Data lakes are usually challenging to operate by those who are not so familiar with unprocessed data. Raw, unstructured data normally need a data scientist and specific tools to learn and translate it for any particular business use.
Alternatively, there is an increasing force behind data preparation tools that generate self-service access to the information saved in data lakes.
Processed data is used in charts, spreadsheets, tables, and more so that most of the employees at a company can understand it. Processed data, like that stored in data warehouses, only need that the users are business professionals familiar with the topic outlined.
Accessibility and security of application point to the use of data repository as a body. Data lakes have no structure and are hence simple to access and easy to adjust. Additionally, any modifications that are made to the data can be done immediately since data lakes have very few restrictions. Data warehouses as they are more structured, one major advantage of data warehouses is that the processing and structure of data make the data itself simpler to decipher, the constraints of structure make data warehouses complex and costly to handle.
The end users of each technology are varied: A data warehouse is managed by business analysts, who question the data through pre-integrated reporting and BI. Business users cannot handle a data lake as efficiently, because data needs processing and analysis to be beneficial. Data experts, data designers, or advanced business users, can obtain insights from large quantities of data in the data lake.
Unlocking business queries using Big Data depends upon the way taken. For instance, if an establishment only appreciates Data Warehouses, then difficulties will be raised to fit handling a Data Warehouse. The on-going discussion of whether to use a Data Warehouse or Data Lake are several, but when observed through the lens of a sharpened Data Architecture Strategy, the options become more well-set.
In reply to a quandary where companies or projects have multiple and diversified data, with many theories, the Data Lake plan has been attached to the toolbox.
The “data lake vs data warehouse” debates has just begun, but the major differences in structure, process, users, and overall agility make each model unique. Depending on a company’s demands, developing the right data lake or data warehouse will be helpful in growth.
The news about Data Lakes reveals several companies need them to stay loose with a tremendously growing market place and with ever-changing data uses and requirements. Many companies can no longer support Data Lakes. So, It is important for businesses to understand both Data Warehouses and Data Lakes and when and how to use them.
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