When to Extend Your Traditional Data Warehouse to Hadoop

With the emergence of technologies like Hadoop, Apache Spark, Pig and Hive in the big data stack, there is an increasing interest surrounding these technologies to process and analyze large volumes of data in real time. Inan earlier era most enterprises were using data warehouses for this purpose. But as data volume and velocity grew bigger, data warehousing started falling short on the scalability and affordability requirements. Hadoop is emerging as a popular data processing framework. While the importance of data warehouses will not diminish in the future, enterprises need to determine the right time to extend them to Hadoop.

The difference between Hadoop and data warehouses

A data warehouse is a reservoir of an enterprise’s data, collected through numerous operational systems- logical or physical. The emphasis is laid on capturing data from different sources for analysis. From a business perspective, data warehouses can produce relevant data faster from multiple sources, thereby enhancing operational systems. Hadoop is an open-source processing framework that can handle several forms of structured as well as unstructured data.

While Hadoop is a big data technology, a data warehouse is an architecture for organizing data and ensuring its integrity. A data warehouse makes the best use of relational and structured data whereas Hadoop excels in managing unstructured data — which traditional data warehouses cannot handle. It is excellent processing unstructured data with minimal response time.

While data warehouses are useful in helping organizations make crucial data-backed decisions, they cannot process complex and unstructured data types like images, social signals, browsing history etc. which are becoming prevalent. Because data warehouses follow schema on a write mechanism, they cannot ingest data without a definite schema. Hadoop, on the other hand, favours schema on reading. This means with a data warehouse, enterprises end up spending a lot of time in modeling data, having stakeholders wait for months to get answers to business questions.

When organizations grow and generate an increasing amount of unstructured data, traditional data warehouses fail to process the complex data. This is when organizations should extend the data warehouse to Hadoop since scalability and cost prove to be a challenge with a data warehouse.

Processing large data sets

Traditional data warehouses are ideal for processing structured data with a fixed schema. But with the rise of big unstructured data, enterprises need a more powerful and streamlined solution. Hadoop is built to store and analyze huge volumes of unstructured data that is complex and pours in from multiple sources.

Because SQL databases can only be scaled vertically, processing large batches of data on a warehouse can be very costly. Moreover, a data warehouse is ill-equipped to handle provisional data sets that are analyzed or used in isolation. In a data warehouse, big data has to first be put through a cleaning and structuring process called ETL (Extract, Transform, and Load), which is a step prone to serious errors. Hadoop offers a scalable solution to meet ever-increasing data storage and processing demands that the data warehouse can no longer handle. With unlimited scale and on-demand access to compute and storage capacity, Hadoop’s tools such as Pig, Spark, Presto, and others are the perfect match for big data processing.

Complex batch vs interactive analysis

If your business needs interactive self-service analytics, the data warehouse proves to be an ideal choice. When the program is a complex one and applications must run in parallel to achieve scale, Hadoop is the right way to go. While a data warehouse can run complex applications in a batch, they do not run in parallel. When large-scale processing becomes complicated, it is time to introduce a Hadoop programmer in the equation.

Fluctuating workloads

When it comes to structured data, a data warehouse is ideal for running constant and predictable workloads. However, it isn’t built to handle massive volumes of unstructured data. To run fluctuating workloads for meeting growing big data demands, enterprises need to deploy a scalable infrastructure wherein servers can be provisioned as per need. Hadoop can quickly spin virtual servers up or down based on demand, thus enabling organizations to handle fluctuating workloads with flexibility and scale.

Combining the data warehouse with the Hadoop platform can be a powerful, cost-effective analytical tool for enterprises. With such a hybrid data infrastructure, they can experience perfect synergy in running small, highly interactive workloads with the data warehouse while using Hadoop to process large and complex data sets for extracting deeper insights. The fact of the matter is that modern organizations cannot sustain on any one single platform to handle the data deluge. While Hadoop is certainly not a replacement for traditional data warehouses, its superior performance and data processing capabilities enable enterprises to reduce costs while maintaining existing applications and leveraging big data for deep business insights.