Designing workflow with Airflow

I have been using Oozie for a while now and was a little dissatisfied with the tool in terms of managing the Hadoop jobs and not to mention  debugging vague errors. While I was analyzing the substitute workflow engine, the Airflow by Aribnb caught my eye. I’ll skip the introduction for now, you can read more about it here. This post highlights a its key features and demonstration of hadoop job.

Before I begin with the example, I’d like to mention the key advantages of Airflow over other tools:

  • Amazing UI for viewing job flow(DAG), run stats, logs etc.
  • You write an actual Python program instead of ugly configuration files
  • Exceptional monitoring options of batch jobs
  • Ability to query metadata and generate custom charts
  • Contributors in the developer community have mostly worked/evaluated the other similar tools, thus it brings the best of everything as the tool evolves.

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ETL with Apache Spark

In continuation to my previous post on Modern Data Warehouse Architecture, in this post I’ll give an example  using PySpark API from Apache Spark for writing ETL jobs to offload the data warehouse.

Spark is lightening-fast in data processing and works well with hadoop ecosystem, you can read more about Spark at Apache Spark home. For now, let’s talk about the ETL job. In my example, I’ll merge a parent and a sub-dimension (type 2) table form MySQL database and will load them to a single dimension table in Hive with dynamic partitions. When building a warehouse on hive, it is advisable to avoid snow-flaking to reduce unnecessary joins as each join task creates a map task. Just to raise the curiosity, the throughput on a stand alone Spark deployment for this example job is 1M+ rows/min. Continue reading

Apache Oozie Configuration with Hadoop 2.6.0

My idea of writing this post is to help people who are trying to install Oozie with Hadoop 2+ environment. As I had to refer different places for fixing the errors which I encountered during the process. Here’s it goes..

Step 1: Download Oozie 4.1 from the Apache URL and save the tarball to any directory

cd ~/Downloads
tar -zxf oozie-4.1.0.tar.gz
sudo mv oozie-4.1.0 /usr/local/oozie-4.1.0

Step 2: Assuming you have maven installed, if not, refer to the installation instructions here

Step 3: Update the pom.xml to change the default hadoop version to 2.3.0. The reason we’re not changing it to hadoop version 2.6.0 here is because 2.3.0-oozie-4.1.0.jar is the latest available jar file. Luckily it works with higher versions in 2.x series

cd /usr/local/oozie-4.1.0
vim pom.xml
--Search for
--Replace it with

Step 4: Build Oozie executable

mvn clean package assembly:single -P hadoop-2 -DskipTests

Step 5: The executable will be generated in the target sub directory under distro dir. Move it to a new folder under /usr/local/

cd ~/Downloads/oozie-4.1.0/distro/target
tar -zxf oozie-4.1.0-distro.tar.gz
sudo mv oozie-4.1.0 /user/local/oozie-4.1.0

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Modern Data Warehouse Architecture

With the changing trends in the world of BI and the Big Data wave everywhere, a lot of organizations have started initiatives to explore how it fits in. To leverage the data ecosystem at it’s fullest potential, it is necessary to think forward and ingest new technology pieces in the right place. That way, in a long run, both business and IT will reap its benefits.

Here’s an interesting prediction by Gartner

By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.

Imagine all the time, money and efforts you’ll save off your existing data and infrastructure components if the Big Data implementation goes well. The architecture diagram below , is a conceptual design of how you can leverage the computation power of Hadoop ecosystem in your traditional BI / Data warehousing processes along with all the real time analytics and data science. They call it a data lake, warehouse is old school now.


Alright, having a Hadoop ecosystem saves the computational time and provides all bells and whistles of real time analytics but “how does it save money? Continue reading

Twitter Sentiment Analysis

Sentiment Analysis in simple words is just reading between the lines of text, a very common technique you use when you read reviews about movies, restaurants etc. to make a choice. This technique is now being highly used by the organizations for pervasive analysis, customer profiling and accurate market campaigning.

While everyone was curiously waiting for the Delhi Elections 2015 results,  during our casual discussion, me and my friend  Harshit Pandey  decided to find out the Tweet trends for the competing parties. We chose Twitter Streaming API as the source for our analysis, Mongo DB for archiving the Tweets, Python for performing sentiment analysis and Tableau Public for visualization.

Here’s how it looks like, please follow the below image link for the interactive version. This dashboard shows the popular hashtags used by the users as well as the trends of the positive negative tweets (specific to each party) before and after the election results were announced.


Algorithm Used: 

To perform the contextual analysis for each party within a tweet we wrote a custom method using decision trees for generating score for the two competing parties. We created a dictionary of biased hashtags and searched for their occurrence in the tweets.  In addition to it we tokenized the tweet text  based on the party name and searched for the occurrence of nearest positive or negative words and assigned the score accordingly. The code can be accessed from the Github repository.

Apache Drill: Writing SQL Queries on NoSQL Databases

Apache Drill is an amazing project by Apache foundation, specifically made to enrich the self-service analytics. It makes querying the semi structured data ridiculously easy for the Business Analysts and Data Scientists. You’d definitely love this if you are working with NoSQL database, Hadoop or scratching your head to write a code for reading a JSON file.  Continue reading

SAP Business Objects 4.1 Audit Dashboard

Amidst of all the amazing data visualization tools available out in the market, I’m writing this post to show what wonders you can do by leveraging Web Intelligence tool from SAP Business Intelligence 4.1 product suite. My idea for this post is not to compare it with any of the Data Viz. tools but just to share something useful for the organizations which are using Business Objects as their main reporting tool.

I have seen Business Objects evolving as a rich product; versions after versions from the ages of BO 6.5 and with the complete hands-on experience on all the versions since then, I feel BO 4.1 is very stable and a mature product of its own race. In order to effectively manage an enterprise product for a global user base, one needs to ensure its availability to the users and should be aware of the platform issues they might encounter in advance.  Out of several methods of system monitoring, I’m sharing a practice that I’d implemented using BO audit universe. Continue reading

How do I begin with Hadoop?

“Tell me and I forget. Teach me and I remember. Involve me and I learn.”

                                                                                   -Benjamin Franklin

I’m a big fan of practical learning, “implement as you learn” is my mantra for learning anything. Hadoop being open source gives the best opportunity for getting your hands dirty as you read about it. There are plenty of free resources online that you can refer to get started with and in this post, I’m going to list and refer some of the good ones I’ve come across.

Getting Started with Hadoop

Depending on your level of interest in learning and exploring Hadoop, you can enroll in any of the free online fundamental courses offered from Big Data University or watch video tutorials form edureka on YouTube. These two sources do not require a sign in from your corporate email id and give a basic overview on what Hadoop is? And of-course the documentation provided by Apache helps in understanding it detail, alternatively you can read the Yahoo Hadoop tutorial. Continue reading

DataWarehouse vs BigData

To Be or not To Be is the question to ask today. I’m not being Hamlet here but with the evolution of Big Data and looking at the current technology trends, I’m curious to discover the cases where it can overcome conventional Data warehouse, where it cannot and most important what are the areas where both DW and BigData can be implemented in conjunction?

Data Warehousing concept has been in place for more than last four decades now  while Big Data, In Memory and the Cloud concept started prevailing in early 2Ks and are in high demand today. Between now and then the data has grown exponentially in the network and the competitive analysis of this data has lead  to evolution of new tools and BI concepts.  Does it mean a slow sunset for DW? I’d say it’s a myth as the business cases differ, an ideal approach will be a blend of both for sure. Continue reading