These Top 10 don’ts of Big Data projects speak of practices that disallow the creation of data lakes for profit. Steer clear of them and go the right way to make your project a success.
While many big data projects offer significant profitability and increase their activities in a short period of time, there are plentiful initiatives that distance themselves from being fool-proof – courtesy these wrong practices.
1. Using Hadoop or Other Denormalized Extract?
Have you been working with large data sets that are relatively flat? Is Hive or any other lesser denormalized RDBMS your preferred schema? Are you storing your files on HDFS? If yes, then even the simplest of selections undertaken by you are resorting to MapReduce and other deoptimized routes for creating table joins. Switch to Hadoop to create faster and more effective extracts.
2. No Clear Cut Enterprise Strategy
Is your Big Data project right at the heart of how your Company plans to utilize its data resources, or is there some mix-up? Big Data projects rarely see success when they are “isolated.” The issues on hand are further exaggerated if there are cloud applications, or other data management priorities that conflict with the strategies on hand. For success, ensure clear cut Big Data formulae, all across the organization!
3. Manual Installation of Nodes?
Are you serious? You surely do not expect to install Hadoop and its parts all on your own, that too in a couple of days? Believe us, there is no such thing like hand-rolling these large-sized data files. To save yourself from oodles of trouble, try using Ambari, Puppet, or something that works for your distribution.
4. HDFS – a File System Only!
Adding data to HDFS is certainly not enough; you have to use the proper tools to draw value from your file system. Well, MapReduce, Pig and Hive will make it work, but you need to understand the ways of securing the data on hand. Also, the stored data should work for all concerned users, the right way.
5. Data Puddles or Data Hubs?
Is your organizational data confined to small puddles (with pre-defined boundaries) dealing with sales, marketing, manufacturing, procurement, etc? Big Data needs to flow freely and is at its best when stored in large data lakes/ hubs. Don’t let policies or politics stifle this essential requirement!
6. HBase is NOT your RDBMS
While checking out Hadoop, you came across a database. Good. Was it HBase or Cassandra? Do know that there’s little in common between your RDBMS and HBase; maybe just a table. Your entire RDBMS schema cannot be represented by HBase. HDFS with Hive is a more winning combination, go for it.
7. Using Big Data for the Wrong Purposes
Most Big Data projects that fail to make it big are either using outdated, conventional data technologies, or are triggering off their projects with overly-ambitious purposes in mind. Avoid both these paths and be practical in your approach and work methods dealing with Big Data; else, you will soon find yourself in a soup
8. MongoDB – is it the Big Data Platform in Use?
No other NoSQL database is as over-rated as MongoDB. Even though this framework works like a Hadoop connector and MapReduce, it fails to provide the right analytical results. Why? That’s because it is but an operational database system and should not be used to obtain high-end data-warehousing technology.
9. Lack of Domain Knowledge
When it comes to handling Big Data hubs, there is an urgent need for the perfect mix of expertise in mathematics/ statistics, knowledge of programming codes, and insights into the domain of operation; be it retail, insurance, banking or any other vertical. So, if your hired data experts hold expertise in Hadoop, but know little about your business, you are destined to be doomed.
10. Less Freedom, No Plan?
Fair enough; you want to evolve with the Hadoop ecosystem. However, have you have left yourself with small choices while dealing with real-time users or data? You are on the wrong path again. Without a proper plan of action to follow, you will reach nowhere. Hadoop requires a methodical and carefully conceptualized approach to database management, rather than mere theoretical inputs. Sketch plans, and then go with the flow.
Now that you are aware of the top 10 don’ts of big data projects, you are better positioned to get yours going great guns. What are the wrong patterns or practices that you have found in your project? Do leave your comments here.