This article Big Data vs Data warehouse will tell you about the top differences between big data and data warehouse. Every day millions and trillions of data are generated. But can we think where this data comes from? Do we imagine where to keep it? The data which is generated has come from multiple sources. This kind of data is very valuable for business insights. Some storage areas are used for keeping our data. Whereas big data is helping out the organization to make use of data to get the best opportunities from that. In this blog, we will discuss 16 differences between big data and data warehouse. Firstly we will know what is big data and data warehouse.
Big data is large information that includes high velocity, high volume, high valued data, and this data comes from different formats
A data warehouse is something that assists with reporting and management purposes. It is considered the main component of business intelligence.
| S.No. | Features | Big data | Data warehouse |
| 1. | Meaning | Big data is a technology that includes 5 V’s i.e are volume, variety, veracity, velocity, and value | Data Warehouse is an architecture that assists in generating reports suitable for business insights. |
| 2. | Types of data | It includes data that is not refined. | It includes data that is processed. |
| 3. | Data Formats | It handles structured and unstructured data that can be from any source. | It mainly handles structured data |
| 4. | Distributed Processing | It allows distributed Processing as it has Hadoop and MapReduce in it. | It does not allow distributed processing. |
| 5. | Types of SQL | Hive SQL or Spark SQL | Normal SQL |
| 6.
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Analytic reports | 0.5% of data is used on analytic reports during loading | 100% data is used on analytic reports during the loading process |
| 7. | Data source | Social media, transactional data, and DBMS data | Only DBMS data |
| 8. | Business point of view | It gives more data and it gives more valuable insights as it takes data from multiple sources | It takes only DBMS data so it worked on some part of the information |
| 9. | Time Processing | As distributed processing is in big data, it takes less time for doing and processing tasks | There is no distributed processing. It takes more time. |
| 10. | Technologies used | Hadoop, Hive, No-SQL DB | Only the relational database and SQL |
| 11. | Hardware | Heterogenous hardware | Homogeneous hardware |
| 12. | Hardware cost | Hardware cost is less | Hardware cost is more |
| 13. | Process Complexity | It is not easy to work, it can’t be manipulated easily | It is easy to work and can be easily manipulated. |
| 14. | Tools | Special kind of tools are mandatory | Traditional database tools are enough |
| 15. | Processing | Analytical and Big data processing | OLTP |
| 16. | Accessibility | Unrestricted access due to HDFS | Limited access |
Conclusion
Data warehouse and Big data are related to each other in some way. Both are valuable in making relevant business decisions. Companies and businesses need data on a daily basis. In this blog, we have discussed 16 differences between big data and data warehouse. If you are having any doubt, feel free to ask me in the comment box.
