Data Replacing Data Warehouse? Busting the myth in 2021

Is Big Data Replacing Data Warehouse? Busting the myth:

The Data Warehouse has been a pioneer over the past decades and today Big Data represents the latest revolution in technology. A common question is whether Big Data Warehousing will replace it.

 Although Big Data and Data Warehousing have similarities, they are two different technologies and there is a big difference between them. Before we see the differences, it is important to know what Data Warehousing and Big Data are. In general, a large data solution is a technology based on volume, speed, and variety, while data warehouses are an architectural concept in the data processing.

First, let’s take a look at the similarities between the two:

• Both contain a lot of data

• Both can be used for reporting

• Both are managed by electronic storage devices

Let’s now see the two technologies:

data collection

Data warehouse refers to data that is retrieved from one or more homogeneous or heterogeneous data sources and then transformed before being loaded into a data repository for data analysis. This data analysis is useful and helps to better evaluate performance and can be used for reporting.

 The repository of the data generated by the process is the data warehouse.

 It is a conceptual architecture designed to store structured, subject-oriented, time-consuming, and non-volatile data for decision making. The data warehouse usually stores historical data, a copy of transaction data structured specifically for query and analysis.

 A data warehouse typically collects data from many transactions and operating systems, which is presented as a consolidated and better version to decision-makers at all levels of the organization. With a well-executed data packaging project, we can obtain, report and analyze this information from all relevant and possible angles; resulting in consistent and accurate information.

Big data

Big data is a technology used to store unstructured data from multiple sources and to manage large amounts of data in exabytes (1 billion GB) and zettabytes (1 billion GB). Big Data can store all kinds of data such as structured, semi-structured, and unstructured data extracted from video, audio, unstructured text, etc. This can happen if you are using cheaper storage devices. The data is not processed in a single location and is distributed across multiple servers for faster processing and is stored in its native format without any planning or modeling. Actual data usage must be applied to the data to get the report.

Big data refers to the volume, variety, and speed of data, the 3Rs mentioned by industry analyst Doug Laney in the early 2000s. Big data is determined by the size of the data, the speed at which it is created, and the size of all data.

• Volume: Organizations have collected a large amount of data from a variety of sources, including business transactions, social media, and sensor information or machine-to-machine data. With new technologies like Hadoop, it is now very easy to archive large amounts of data.

• Speed: All collected data flows at unprecedented speeds and must be processed in time. RFID tags, sensors, and smart meters require all data to be processed in the near future.

Variety – data streams collected in different formats – some can be structured, some numeric data in traditional databases, and others can be completely unstructured text documents. This can be in the form of email, video, audio, inventory data, and financial transactions.

Finally, let’s see how Data Warehouse and Big Data differ

• The data is stored in a structured way in the Data Warehouse, in unstructured Big Data.

• Data quality is converted to Data Warehouse, while Big Data contains raw data

• The Data Warehouse stores large amounts of data, while Big Data stores large amounts

• The storage costs of the data warehouse are relatively high, while the storage costs of big data are low

• The data in a data warehouse is very secure, Big Data is not; it’s open-source security and it keeps getting better.

Big Data technologies focus on advanced analytics and can be seen as a strategy for modernizing databases. Data warehouses are built primarily for reporting, OLAP, and performance.

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