Will Hadoop replace Data Warehouse in the near Future?

Boost your career with Free Big Data Courses!!

Hadoop or Data Warehouse – which one is best for you?

With Hadoop’s ability to scale to large data size and analytics power, we can think Hadoop as an obvious candidate to the data warehouse. But as we dig deep into the matter we find some other facts. Let us find out how and where does Hadoop stand in front of a traditional data warehouse system. And can it function as a data warehouse itself? or is it capable to replace data warehouse?

Hadoop and Data warehouses

When to Use Hadoop and Data Warehouse?

Nowadays three major systems are in common use. They are

  1. Hadoop
  2. Data Warehouse
  3. MPPs (Massively Parallel Processing systems).

Each uses SQL in different ways.

1. Data Warehouses

Data Warehouses are essentially large database systems. They are essentially relational databases which are very SQL-friendly. They provide good performance and relatively easy administration. This is because of its symmetrical multiprocessing architecture. It routes all operations through a single processing node. And shares memory and operating system.

The biggest drawbacks are cost and flexibility. We have to build most of the data warehouses upon proprietary hardware and hence is very expensive. Traditional data warehouses operate on the concept of schema-on-write. The data model should already be present while writing the data. They have fixed schemas and are not very flexible in handling unstructured data. They are good at making transactional analytics. We can make decision-based upon a defined set of data elements. But it lags behind when it comes to recommendation systems. As in this relationships are not well defined.

2. MPP Data Warehouses

MPP data warehouses are the evolution of traditional data warehouses. It makes use of multiple processors lashed together via common interconnect. MPP architecture shares nothing between these processors. Each server has its own operating system, memory, processor and storage. A master processors coordinate between other processors and distribute data across the nodes. It also coordinates actions and results between these processors.

MPP data warehouse is highly scalable. The addition of processors increases the performance in a linear fashion. MPP architecture is suitable for working with multiple databases simultaneously. This makes them more flexible than traditional data warehouses. Like traditional data warehouses, they can work with structured data in the fixed schema.

MPPs require sophisticated engineering most of which is proprietary to individual vendors. This makes them expensive to implement. In order to realize maximum performance gains the in MPPs rows get spread across processors. Many MPP vendors hide this detail in their SQL instances.

3. Hadoop

Hadoop and MPP data warehouses have similar architecture. But still has some significant differences. Processors are loosely coupled across the Hadoop cluster and can work with different data sources. In Hadoop data manipulation engine, data catalog and data storage engine are independent of each other. Also important is that Hadoop can easily accommodate structured as well as unstructured data. This makes it ideal for iterative inquiry. They need not define analytics output based on narrow constructs defined by the schema. Business users can experiment to find out what matters most to them.

Know more about Hadoop and its Working 

Hadoop Vs Data Warehouse

Hadoop is better than EDW (Enterprise Data Warehouse) in terms of flexibility, efficiency, and cost-effectiveness. Below we will discuss the areas where Hadoop has an edge over traditional data warehouse system.

  • We can load data in Hadoop without having a data model in place. In traditional EDW system first, the database design should be in place to load the data. But Hadoop works on the schema-on-read concept and doesn’t require a data model to be in place.
  • We can apply a data model based on questions asked on data. One need not define the schema while loading the data. And once we load the data we can look through our own lens based on the analytical question in hand.
  • They have designed Hadoop to ask questions as they appear to the user. So as and when requirements arrive we can build ad hoc queries over Hadoop. Hadoop ecosystem components like Hive has this facility.
  • Hadoop can store and analyze both structured and unstructured data. The traditional EDW systems can process only structured data stored in the tables. Which are in rows and columns format.
  • We can analyze data to support diverse use cases. We can do many types of analysis through Hadoop and its components like predictive analysis, graphical analysis and so on. On the other hand, conventional EDW system can do only arithmetic computations it does not have the facility to work with the graph. Also, it does not have the concept of machine learning algorithms weaved into it.
  • Hadoop is an open source software and has no license fees. On the other hand, the EDW system is expensive to buy and implement. We got to pay for the software and the high-end hardware too.
  • Hadoop runs on commodity hardware. Whereas EDW runs on machines with good configurations which are not cost effective. We can scale Hadoop linearly very easily without any downtime. This means we can add additional machines to the cluster on the fly. EDW can be horizontally scaled requiring downtime of the system.

Can Hadoop Replace a Data Warehouse?

There is one significant thing to bear in mind. It is that big data analytics is not going to replace traditional structured analytics. We can produce significant business value from the innovative solution that comes up by blending big data analytics with traditional data sources.

Imagine a hotel chain joining web-based property search with its historic occupancy metrics in RDBMS. In order to decide the optimum nightly price and boost its revenues.

No replacement but coexistence. This is the correct way to view the relationship between RDBMS, MPPs, and Hadoop. Organizations today focus on Hadoop distributions that optimize data flow between Hadoop-based data lakes and traditional systems. In other words today they follow keep old and innovate the new principle.

Hadoop as a Data Warehouse

The distributed file system at the heart of Hadoop, HDFS is not designed for speed and updateability requirement of data warehouses.

  • Immutability Problem

We cannot change data in Hadoop i.e. it cannot be overwritten or appended. But data warehouses require to change table anywhere from daily to multiple times in a second. Hadoop has various workarounds implemented to eradicate this situation. But they are simply very slow cumbersome and unreliable.

  • Methods to Accommodate Change to Data

To make Hadoop function as Data Warehouse we can use two approaches. One is a time-based partitioning and/or four-step method.

Under time-based partitioning data in HDFS gets organized in files based on month, year or any other unit of time. If record changes in the originating database for let say a month of July. Then the entire file for the month of July is overwritten which is too costly to perform for a change in a single record.

The four-step method takes a more complex approach. We can create a temporary table out of the new data from the originating system. This table is then joined with an existing table in a view to overwrite the existing data in HDFS.

Don’t forget to check the latest Hadoop HDFS Tutorial

  • Resolving Immutability

Cloudera recognized the problem that immutability of data in HDFS caused its customers. They put efforts in this direction and came up with a solution that allowed to makes changes to the data in a practical way. The result was kudu that acts like an updateable file store. Commands like INSERT, UPDATE and DELETE which never existed with HDFS and HIVE now function as expected. Scalability to the enormous size of data is also an added feature along with this.


The Enterprise Data Warehouse has become an integral part of enterprise data architecture. However, the volume and complexity and data has posed a few challenges to the efficiency of EDW systems. The system should be able to manage the complexity of data from various sources like social media, web, IoT and more. The solution should also be able to handle the integration of new data sources into existing EDW Systems. EDW optimization using Hadoop provides a cost-efficient environment with more flexibility, scalability, and optimal performance.

Clear with the concept of Hadoop and Data Warehouse? Hope you got your answer. Still, if you have any query, ask freely through the comment section. We will definitely get back to you.

This is the right time to start your Hadoop training with industry experts

Did we exceed your expectations?
If Yes, share your valuable feedback on Google

follow dataflair on YouTube

Leave a Reply

Your email address will not be published. Required fields are marked *