Big Data vs Data Warehouse - Find Out The Best Differences
The Difference Between Big Data vs Data Warehouse, are explained in the points presented below:
| BASIS FOR COMPARISON | DATA WAREHOUSE | BIG DATA |
| Meaning | Data Warehouse is mainly an architecture, not a technology. It extracting data from varieties SQL based data source (mainly relational database) and help for generating analytic reports. In terms of definition, data repository, which using for any analytic reports, has been generated from one process, which is nothing but the data warehouse. | Big Data is mainly a technology, which stands on volume, velocity, and variety of the data. Volumes define the amount of data coming from different sources, velocity refers to the speed of data processing, and varieties refer to the number of types of data (mainly support all type of data format). |
| Preferences | If an organization wants to know some informed decision (like what is going on in their corporation, next year planning based on current year performance data etc), they prefer to choose data warehousing, as for this kind of report they need reliable or believable data from the sources. | If organization need to compare with a lot of big data, which contain valuable information and help them to take a better decision (like how to lead more revenue, more profitability, more customers etc), they obviously preferred Big Data approach. |
| Accepted Data Source | Accepted one or more homogeneous (all sites use the same DBMS product) or heterogeneous (sites may run different DBMS product) data sources. | Accepted any kind of sources, including business transactions, social media, and information from sensor or machine specific data. It can come from DBMS product or not. |
| Accepted type of formats | Handles mainly structural data (specifically relational data). | Accepted all types of formats. Structure data, relational data, and unstructured data including text documents, email, video, audio, stock ticker data and financial transaction. |
| Subject-Oriented | Data warehouse is subject oriented because it actually provides information on the specific subject (like a product, customers, suppliers, sales, revenue etc) not on organization ongoing operation. It does not focus on ongoing operation, it mainly focuses on analysis or displaying data which help on decision making. | Big Data is also subject-oriented, the main difference is a source of data, as big data can accept and process data from all the sources including social media, sensor or machine specific data. It also main on provide exact analysis on data specifically on subject oriented. |
| Time-Variant | The data collected in a data warehouse is actually identified by a particular time period. As it mainly holds historical data for an analytical report. | Big Data have a lot of approach to identified already loaded data, a time period is one of the approaches on it. As Big data mainly processing flat files, so archive with date and time will be the best approach to identify loaded data. But it have the option to work with streaming data, so it not always holding historical data. |
| Non-volatile | Previous data never erase when new data added to it. This is one of the major features of a data warehouse. As it totally different from an operational database, so any changes on an operational database will not directly impact to a data warehouse. | For Big data, again previous data never erase when new data added to it. It stored as a file which represents a table. But here sometime in case of streaming directly use Hive or Spark as operation environment. |
| Distributed File System | Processing of huge data in Data Warehousing is really time-consuming and sometimes it took an entire day for complete the process. | This is one of the big utility of Big Data. HDFS (Hadoop Distributed File System) mainly defined to load huge data in distributed systems by using map reduce program. |
As per above explanation and understanding, we can come below conclusion:
