What’s The Right Choice: Big Data Or Enterprise Data Warehouse?

Enterprise Data Warehouse (EDW) is currently buzzing and Big Data is the most recent trend in this technological world. Many think big data will replace older data warehousing, another reason to think this is that they have many similarities. Both hold an enormous measure of data that could be used for reporting and are additionally managed by electronic storage gadgets. Still, EDW and Big Data are not compatible. One of the major differences between the two is Data Warehousing is an architectural concept in data computing whereas the Big Data Solution is technology.

A company can have different combinations of Big Data and Data warehouse depending upon four consideration factors like Unstructured Data, Data Structure, Data Volume, Schema-on-Read.

We have mentioned the differences and similarities between Big Data and EDW and are illustrated with a Use Case example. 

Data Warehouse

Data Warehouse means the data obtained from one or more homogeneous and heterogeneous data sources, changing it and stacking it into a data repository to improve business decisions through data analysis. The data repository which generates is nothing but it is a data warehouse only.

This changed data is purified, upgraded and applied business rules; analysis is done in ELT / ETL stage to stack it into an organized structure. It stores historical data, copy of transaction data usually structured for analysis and query. The tangible data consolidation is shifting to logical one and real-time data accompanies it too. If the design of the enterprise data warehouse is done properly then it enables us to analyze access and report that data from all the significant and possible points. Also, the determined data is precise and predictable. 

Big Data

This custom software development technology stores the unstructured data from several sources, manage large data volume in Zettabytes and Exabytes. Big Data can store structured, unstructured, and semi-structured data highlighting the unstructured text in the content, video, sound, etc., with the utilization of cheaper storage devices. For faster processing, the data is distributed and decentralized across many servers, this data is stored in a native format, rules are applied and report is generated. Volume, Velocity, and Variety are three key 3 Vs of Big Data.

Difference between Data Warehouse and Big Data

• Security: DW is highly secured and Big Data has open-source security that is constantly growing

• Data Type: DW stores structured, a schematic form of data and Big Data holds unstructured data such as videos, logs, audio, etc.

• Quality: DW provides transformed data whereas Big Data provides raw data

• Cost: Storage cost is comparatively high in DW and cheaper in Big Data

• Storage: DW store huge amount of data and Big Data stores enormous volumes

Today, data is very huge and increasing rapidly, also characterized by Velocity, Variety, Volume, and Veracity, it has changed the way data is gobbled radically. Example – According to reports of Facebook around 2.5 billion items are shared or exchanged every day; their data is also rapidly increasing at the rate of 500TB per day. They also claim to capture every user click in their database.

Due to these growing needs, the challenge to extract and store value data emerges; it involves quality, accuracy, cost, and maintenance.

Data Warehouse or Big Data?

• Data warehouse

To make the right and informed decisions, organizations need DW. To know what is exactly going on in your organization, you require reliable and believable data that is accessible to all.

• Big data

Plenty of corporations have huge data that craves the need to use Big Data. The organization can make better decisions, earn more profit, revenue and more customers if this data is unlocked in the right way and can contain more valuable information. This is exactly what most corporations want.

Both look similar but have a clear difference, Big Data is a repository to carry huge data but it is not sure what we want to do with it, whereas data warehouse is specifically designed with an intention to make informed decisions. Further, Big Data can be used for data warehousing purposes.

Use Case Example

A Financial services company generates structured data (transaction history and customer demographics) and unstructured data (customer behavior) on social media and websites. In some cases, where companies depend on time-sensitive data analysis, a traditional database DWH is a better choice for structured transaction history and customer demographics. In case fast performance is not critical, Big Data analysis perfect fit for unstructured and structured customer transactions or behavioral data.

Can EDW and Big Data/Hadoop share the same umbrella?

Organizations know the requirement to combine their business with traditional data warehouses, with less structured and big data sources at one side and their historical business data sources on the other side. A hybrid model supporting big data and traditional sources can achieve these business goals.

The highly structured and optimized operational data lies in a perfectly controlled DW whereas the highly distributed data which changes in real-time is handled by Hadoop infrastructure. The application to embed big data and SQL analytic processing to allow deeper insights on multi-structured data sources with scalability and high performance is Teradata Aster Big Analytics Appliance.

With the Hybrid approach firms also secure their investment in their DWH infrastructure and extend to fit in the Big Data environment. Hadoop is made with a group of products each having multiple capabilities. Several areas in a data warehouse architecture like Data Archiving, Data Staging, Schema Flexibility, etc., Hadoop products can contribute. Hadoop as a data platform is more compelling for storing and capturing big data in a DW environment, to process that data for analytic purposes on other platforms.

Approach to step up DWH in an organization with Hadoop/Big Data cluster is:

• Continue storing back-office systems and structured data from OLTP into DWH.

• Storing unstructured data (all of the communications with customers i.e. customer feedbacks, phone logs, GPS locations, emails, text messages photos, tweets) into Hadoop/NoSQL.

• Co-relating the data from both DWH and Hadoop clusters for better insight about products, equipment, customers, etc. Now, against this co-related, organizations can run ad-hoc analytics, targeting and clustering models data in Hadoop, which is quite intensive computationally.

Conclusion

Modernization strategy for data archives, Big Data technologies focus on advanced analytics; Data Warehouses were built for OLAP, performance management and reporting. Hence, Big data and DW, are not the same and therefore not interchangeable. An organization can use them depending on business needs.

Hadoop may replace an equivalent data platform like a relational database management system and not a data warehouse because platform and data are non-equivalent layers in DW architecture.