Today, everyone talks about storing data in the raw format so that it can be analyzed and generate insights at a later point of time. Fantastic Idea. Data Lakes just delivers that promise. However, the complexity of data is increasing day by day. And there are these new data sources that are getting added on a regular basis.
Not every day you end up dealing with data sets which you are familiar with. Considering the kind of new type of data that gets added, most likely that one would end up dealing with data sets out of their comfort zone.
Data science teams spend most of their time with exploring and understanding data.
- If you must deliver some quick insights on a set of data will you go through them manually to figure out or can we do something within the data lake that can be used?
- What would be an easy way for the Data Science team to understand the data set quickly, understand patterns, relationships so that we could generate some hypothesis?
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
- Maximize insight into a data set;
- Uncover underlying structure;
- Extract important variables;
- Detect outliers and anomalies;
- Test underlying assumptions;
- Develop parsimonious models; and
- Determine optimal factor settings.
Via : http://www.itl.nist.gov/div898/handbook/eda/section1/eda11.htm
Exploratory Data Analysis : http://www.statgraphics.com/exploratory-data-analysis
If you google about exploratory data analysis, you will get tons and tons of material about doing EDA using R or Python.
Considering the shortened timelines, if you expect your data science teams to develop code to understand, you may not be able to deliver value at the speed in which your business is expecting results.
There are couple of tools which may help you understand your data faster. Google Data Profiling and you will get tons and tons of results on this topic. My favorite tools right now in this topic are
Both are easy to use with a simple user interface. You can use the free version to get started. If you have an automated data pipeline using SPARK, you can also generate the profile statistics about the incoming data and store it as part of your Catalog.
I really like this presentation on this topic.. Data Profiling and Pipeline Processing with Spark.
Once you do this with Spark, you may want to update the data profile information and store it as part of your catalog. If you index your catalog with Elastic Search, you may be able to provide an API for your Data Science teams to search for the files with certain quality attributes etc.
The above tools will help you get a quick understanding of your data. But, what If you want pointers for analysis to get started about your data? Only a profiler will not help in this case. You may want to explore this product from IBM (yeah… you heard it right… it’s from IBM and I am using it daily). Check it out here… IBM Watson Analytics
Watson Analytics – is a SMART discovery service and it is super smart. It is available for $80 User/Month. For the value, you get out of it, $80 per month is really nothing.
You can use it for data exploration and predictive analytics and it is effortless. A free one month subscription is available for you to play with.
I have looked around various products and I couldn’t find anything which is closer to what Watson offers. If i have to mention about a drawback, it doesn’t provide connectivity to S3. You may have to connect to Postgresql or Redshift to extract data.
If you can integrate it in your platform and use it effectively, you will be able to add value to your customers in literally no time.