Difference between Big Data and Data Warehouse
Big Data and Data Warehouse both are used as main source of input for Business Intelligence, such as creation of Analytical results and Report generation, in order to provision effective business decision-making processes. Big Data allows unrefined data from any source, but Data Warehouse allows only processed data, as it has to maintain the reliability and consistency of the data. The unprocessed data in Big Data systems can be of any size depending on the type their formats. Almost all the data in Data Warehouse are of common size due to its refined structured system organization.
Head to Head Comparison between Big Data vs Data Warehouse
Below is the Top 8 Difference Between Big Data vs Data Warehouse
Key Differences between Big Data vs Data Warehouse
The Difference Between Big Data vs Data Warehouse, are explained in the points presented below:
- Data Warehouse is an architecture of data storing or data repository. Whereas Big Data is a technology to handle huge data and prepare the repository.
- Any kind of DBMS data accepted by Data warehouse, whereas Big Data accept all kind of data including transnational data, social media data, machinery data or any DBMS data.
- Data warehouse only handles structure data (relational or not relational), but big data can handle structure, non-structure, semi-structured data.
- Big data normally used a distributed file system to load huge data in a distributed way, but data warehouse doesn’t have that kind of concept.
- From a business point of view, as big data has a lot of data, analytics on that will be very fruitful, and the result will be more meaningful which help to take proper decision for that organization. Whereas Data warehouse mainly helps to analytic on informed information.
- Data warehouse means the relational database, so storing, fetching data will be similar with a normal SQL query. And big data is not following proper database structure, we need to use hive or spark SQL to see the data by using hive specific query.
- 100% data loaded into data warehousing are using for analytics reports. But whatever data loaded by Hadoop, maximum 0.5% used on analytics reports till now. Others data are loaded into the system, but in not use status.
- Data Warehousing never able to handle humongous data (totally unstructured data). Big data (Apache Hadoop) is the only option to handle humongous data.
- The timing of fetching increasing simultaneously in data warehouse based on data volume. Means, it will take small time for low volume data and big time for a huge volume of data just like DBMS. But in case of big data, it will take a small period of time to fetch huge data (as it especially designed for handling huge data), but taken huge time if we somehow try to load or fetch small data in HDFS by using map reduce.
Big Data vs Data Warehouse Comparision Table
| 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 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 a 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 | A 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 the 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 has a lot of approaches to identified already loaded data, a time period is one of the approaches on it. Big data mainly processing flat files, so archive with date and time will be the best approach to identify loaded data. But it has 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 sometimes in case of streaming directly use Hive or Spark as an operation environment. |
| Distributed File System | Processing of huge data in Data Warehousing is really time-consuming and sometimes it took an entire day to 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. |
Conclusion
As per above explanation and understanding, we can come below conclusion:
- Big data and data warehouse are not same, so it not interchangeable.
- An organization can follow Big Data and Data Warehouse solution based on their need, not because they are similar.
- An organization can follow the combination of both big data as well as data warehouse solution as per their need.
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