Updated April 28, 2023
Difference Between Big Data vs Data Warehouse
Big Data and Data Warehouses are the main input sources for Business Intelligence, such as creating Analytical results and Report generation, to provide 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. Due to its refined structured system organization, almost all the data in Data Warehouse are of common size.
Head-to-Head Comparison Between Big Data vs Data Warehouse
Below are 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 is explained in the points presented below:
- Data Warehouse is an architecture of data storing or data repositories. Big Data is a technology that handles vast amounts of data and prepares the repository.
- A Data warehouse accepts any DBMS data, whereas Big Data accept all kinds of data, including transnational data, social media data, machinery data, or any DBMS data.
- Data warehouse only handles structured data (relational or not relational), but big data can handle structured, non-structure, and semi-structured data.
- Big data typically uses a distributed file system to load huge data in a distributed way, but a data warehouse doesn’t have that 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 will help to make proper decisions for that organization. Whereas Data warehouse mainly allows to analytic on informed information.
- Data warehouse means the relational database, so storing and fetching data will be similar to a standard SQL query. And big data is not following proper database structure; we need to use hive or spark SQL to see the data using a hive-specific query.
- Analytics reports use 100% of the data loaded into data warehousing. However, Hadoop has only utilized a maximum of 0.5% of the data loaded for analytics reports thus far, while the remaining data has been loaded into the system but remains unused.
- Data Warehousing can never handle humongous data (totally unstructured data). Big data (Apache Hadoop) is the only option to handle massive data.
- The timing of fetching increases simultaneously in the data warehouse based on data volume. This means it will take a small amount of time for low-volume data and a big time for a huge volume of data, just like DBMS. Due to its specialized design, big data can quickly fetch vast amounts of data. However, it can take significant time to load or bring small data in HDFS using map-reduce.
Big Data vs Data Warehouse Comparision Table
Below is the comparison table of Big Data vs Data Warehouse:
| Basis For Comparison | Data Warehouse | Big Data |
| Meaning | Data Warehouse is mainly an architecture, not a technology. It extracts data from various SQL-based data sources (primarily relational databases) and helps generate analytic reports. In terms of definition, a data repository used for analytic reports has been generated from one process: the data warehouse. | Big Data is mainly a technology that 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 supporting all types of data format). |
| Preferences | Suppose an organization wants to know some informed decisions (like what is going on in their corporation, next year’s planning based on current year performance data, etc). In that case, they prefer to choose data warehousing, as for this kind of report, they need reliable or believable data from the sources. | Suppose an organization needs to compare with a lot of big data, which contains valuable information and helps them to make a better decision (like how to lead to more revenue, more profitability, more customers, etc). In that case, they prefer the 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 products) data sources. | Accepted sources include business transactions, social media, and sensor or machine-specific data information. It can come from a DBMS product or not. |
| Accepted Types of formats | Handles mainly structural data (specifically relational data). | Accepted all types of formats. Structure, relational, and unstructured data include text documents, email, video, audio, stock ticker data, and financial transactions. |
| Subject-Oriented | A data warehouse is subject-oriented because it provides information on a specific subject (like a product, customers, suppliers, sales, revenue, etc.), not the organization’s ongoing operation. It does not focus on ongoing operations. It mainly focuses on analyzing or displaying data, which helps in decision-making. | Big Data is also subject-oriented; the main difference is the data source. Big data can accept and process data from all sources, including social media, sensor, or machine-specific data. It is also mainly to provide an exact analysis of data specifically on subject-oriented. |
| Time-Variant | The data collected in a data warehouse is identified by a particular period. As it mainly holds historical data for an analytical report. | Big Data has a lot of approaches to identifying already loaded data; a period is one of the approaches on it. Big data mainly process flat files, so archiving with date and time will be the best approach to identify loaded data. But it can work with streaming data, so it does not always hold historical data. |
| Non-Volatile | Previous data never erase when new data is added to it. This is one of the significant features of a data warehouse. As it is different from an operational database, any changes on an operational database will not directly impact a data warehouse. | For Big data, previous data never erases when new data is added. It is stored as a file that represents a table. But here, sometimes, when streaming directly, use Hive or Spark as an operation environment. |
| Distributed File System | Processing huge amounts of data in Data Warehousing is time-consuming and sometimes takes a day to complete. | This is one of the big utilities of Big Data. HDFS (Hadoop Distributed File System) primarily loads massive amounts of data into distributed systems using a map-reduce program. |
Conclusion
As per the above explanation and understanding, we can come below conclusion:
- Big data and data warehouses are not the same, so it not interchangeable.
- An organization can follow Big Data and Data Warehouse solutions based on their need, not because they are similar.
- An organization can follow the combination of both big data and data warehouse solutions as per their need.
Recommended Articles
This has been a guide to Big Data vs Data Warehouse. Here we have discussed Big Data vs Data Warehouse head-to-head comparison, key differences, infographics, and a comparison table. You may also look at the following articles to learn more –
