Knowledge Hub

Data Rooms or Datastore are both the same entity. Data Rooms are normally used for due diligence of confidential documents like handling Business Mergers or Acquisitions by bidders & advisers, compliance & audit teams.

Traditionally, there are Physical Data Rooms with Physical Documents. These Physical Data Rooms are for restricted people only, secured by guards with a maximum of one or two parties to go in at any given point-in-time which delays the process if multiple parties are using the same Physical Data Room.

With the growing businesses globally, it was a very expensive affair for multiple teams to fly over to be near these Physical Data Rooms. As an alternate and more viable solution, VDRs (Virtual Data Room) came into the picture which are available Online, behind Firewalls and with proper Access Control. VDRs have changed the game, now sellers can invite a bigger audience and teams sitting in one part of the world to participate in a bid process happening in the other part of the world. VDRs have cut down the cost and expedited the process.

Hadoop and its relationship with Big Data. The Big Data ecosystem has many components like NoSQL databases, In-Memory process, MPP, 3 Vs etc. but Hadoop is the backbone, and it was the main reason for the popularity of Big Data. In other words, Hadoop was the Selling Pitch for Salespeople.

'Hadoop is a Java-based framework.' Currently, its latest version is Apache Hadoop 3.3.4.’

Please note, Hadoop is not a solution, it's again an EMPTY storage. One must load data first just like we do in traditional databases before any analytics can be generated. For the first 5-7 years, Sales keep selling Big Data in such a way that you buy Big Data, and it will do magic for you, where all kinds of Analytics will be readily available but no it didn't happen, and you already bought it lolz. Learning: For every new technology, first hire a specialist internally and then invest 😊.

Let's go a little deeper.

Obvious question, difference between Hadoop, HDFS and Hive?

Hadoop is an Open-Source Framework to store structured, semi/unstructured data. Default block size of 128MB (changeable).

HDFS is a Hadoop Files System just like FAT, NTFS, EXT etc.

Hive is only a wrapper on top of files stored in HDFS. Hive behaves just like tables in traditional databases, but it doesn't store any data, it just manages table structures on top of HDFS files. There are 2 ways Hive works, either store files in HDFS directly or create tables which will create HDFS files at the back end. The difference in these approaches is 1) each file stored in HDFS creates its own block so if there are 2 small files e.g., 50 MB each, then there will be 2 blocks created. 2) on the other hand, Hive manages its own blocks so if there are 2 small files of e.g., 50 MB each, then Hive will store both in a single block.

Consider 1000s of 1MB files float in from social media means 1000s of 1MB small blocks created impacting MPP performance. The best practice is to have blocks of 128MB.

Dormant Data and Dark Data are mostly confused with each other.

Dark Data

‘Data that is not utilized is Dark Data.’

Dark Data is data that is not in use due to lack of technology and is not a Data Asset. Dormant Data is the data, which is useable, but decision-makers don't use it, or it’s not required for analysis but that doesn't mean you can delete it or purge it, so we keep it in Tape drives, in Hadoop and nowadays Object Storage is also good choice.

Before the introduction of Big Data, the % age of Dark Data was around 90% as it included semi and unstructured datasets and sometimes structured as well. For example, JSON or XML files, Video or Audio files, Logs, Metadata etc., all these datasets were there but technologies were either not there or the affair to analyze those were very expensive. The only reason companies kept this kind of data was for Compliance or Audit purposes. Since the Big Data ecosystem has come onto the surface and hardware has become dead cheap so hosting and processing these datasets has become possible.

As we discussed in topic i.e., Dark Data, that it is utmost important for a CDO (Chief Data Officer) to minimize Dark Data as much as possible, in the organization. In other words, the more the Dark Data, the more the cost without any ROI. Time is not far when Dark Data Management will become the most essential and obvious KPI for CDO (Chief Data Officer).

Now the questions are.

• Is all Dark Data bad? NO.
• Is all Dark Data delete able or purge able? Yes and No.

‘Data Detritus means that data, which is no more required, which is of no use anymore etc.’

• Dark data is something which was and is, not in-use.
• Dormant data is something which is hardly in-use.
• Detritus data is something which was in-use but will not be in-use, anymore.

In other words, Detritus data was once valuable before it became Dormant, as compared to traditional Dark Data (explained separately) which was never in-use due to technologies barriers etc. Detritus data is normally not deleted or purged as it can still be required for Regulatory or Compliance parties.

We all have heard about Mean and Median values but not Spread.

Before jumping into Spread, let's have a quick definition of Mean and Median which will assist in understanding Spread.

• Mean: The mean (informally, the “average “) is found by adding all the numbers together and dividing by the number of items in the set: (10 + 10 + 20 + 40 + 70) / 5 = 30.
• Median: The median is found by ordering the set from lowest to highest and finding the exact middle. The median is just the middle number: 20.
• Spread: The simple explanation of Spread is e.g., how variable the data is or how spread out the data values are in the dataset, or it is also referred to as a measure of dispersion etc., from the Mean or Median values.

Let’s try to understand with an example. Let’s say we have a dataset with the customers’ age from 20 to 60.

Breakdown

• 8 customers with Age [10]
• 15 customers with Age [20]
• 12 customers with Age [30]
• 7 customers with Age [40]
• 3 customers with Age [60]

This is again a very crucial topic but is heavily ignored in planning to host data in the big data ecosystem. It can impact the performance of analysis and even data pipelines execution.

Most of us know database tables are used to store data but at the backend where it's stored, we normally don’t bother. As far as databases are concerned, it is still fine to ignore the file format behind it but when it comes to Big Data it’s of utmost importance to plan the right File Formats for the right Data processing.

There are normally two kinds of data usage 1) to do ETL/ ELT 2) to extract data.

• Row File Format: In row format type, when the user requests to extract data from one column, the whole row is extracted in memory and a particular column is displayed on the screen. 
• Columnar File Format: In columnar format type, when a user requests to extract data from one column, only that column data is extracted in memory and displayed on screen.

An Index is used to enhance the performance of database queries on tables. We can create as many indexes as possible we need or want on a particular table. Please note, in contrast, having more than required indexes can decrease performance as well.

Normally, an index is created when the database starts growing.

Why are indexes needed?

A famous example for better understanding; imagine walking into the Library of Congress and be given the task of finding a specific publication within 10 minutes. Would you be able to complete this task within the given time frame? The Library of Congress is considered the largest library in the world and it houses approximately 170 million items. Now, if you are a smart chap like me 😊, the first thing we would do is ask for access to the library’s index (which can be manual list or a list in a computer system but a list of names and locations of each book in the library) because indexes contain all the necessary information needed to access items quickly and efficiently. In the same manner, a database index contains all the necessary information to access data quickly and efficiently.

Partitioning is a data organization scheme in which table data is divided across multiple storage objects called data partitions according to values, in one or more table columns. Each data partition is stored separately. It consists of its data, indexes, configuration files, and transaction logs. For example, there is a Salary table with 10 years of data. It can be partitioned yearly. At the backend, at the storage level, this table will be divided into 10 smaller datasets i.e.,

1 for each year. Keynote here is, all partitions will be stored wherever the empty slots are, maybe 4 partitions including years 2 & 3 got stored in 1 node of 10 nodes-cluster. This is called Shared-Architecture. Now, a query for years 2 & 3 will go and hit only two partitions making it much faster but as both partitions were on 1 node so one node processing is used.

Partitioning Techniques

• Single-level Partitioning
  • Hash Partitioning
  • Range Partitioning
  • List Partitioning
• Composite Partitioning
  • Range–Range Partitioning
  • Range–Hash Partitioning
  • Range–List Partitioning
  • List–Range Partitioning
  • List–Hash Partitioning
  • List–List Partitioning

Normally, ACID and Insert/Update/Delete are mixed, but they have different functions and at the same time work together.

There are 4 traits of ACID i.e., atomicity, consistency, isolation, and durability.

• Atomicity: It makes sure data is completely written in a destination with no partial data left behind.
• Consistency: It makes sure that once an application performs an operation, the results of that operation are visible to every next query. For example, sometimes a user is reading a particular data and at the same time it might get updated or even may get deleted. This should not impact the user query consistency.
• Isolation: An incomplete operation by one user does not cause unexpected side effects for other users.
• Durability: Once an operation is completed, it will be preserved even in the event of the machine or system failure. For example, in databases there is a concept of Transaction Log which holds every query statement run in the database so in case data is lost, it can still be reproducible.