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Mr. Navratan Singh Verma, Chemical engineering, 1967 batch, IIT Bombay, expired on 9th March 2019 at the age of 75 years. He was admitted to a hospital in Bulandshahar U.P. on 28th February and expired on 9th March due to acute blood infection in the hospital itself.
Mr. Verma was a very hardworking, honest and dedicated engineer. His work ethic was extraordinary and commendable. Due to this he was not very social and had relatively fewer friends. During our graduate studies at IIT from 1962 to 1967, Mr. Verma, Mr. Radheshyam Goswami and I, Gajraj Singh Sachan, lived in the same hostel no. 6 and also were in the same wing and same floor. We all maintained close cordial contacts in later part of life as well.
Mr. Verma worked for over a decade initially in two chemical companies, first in a sugar mill in Kopargaon in Maharashtra about five years and later in another sugar company in Uttar Pradesh. Subsequently, he started his own consultancy company for setting up very economically viable small chemical plants in and around Delhi area. As for as we recollect, he established many Oxalic Acid manufacturing plants, Antimony Oxide plants, Organic, Oxygenated and Halogenated Solvent plants. He continued with his consultancy work until the end.
Mr. Verma was highly appreciative and had very high regards for Prof. N.R. Kamath. Whilst working in a sugar mill in Maharashtra, he met Prof. Kamath on many occasions to seek his advice on various engineering issues. His work and also his health suffered significantly after passing away of his wife in 2011 and his elder son in an accident in 2015. Now, he is survived by his two other sons and a daughter.
During the last five months, I tried contacting him many times but of no avail. Finally, it was by chance that when I called his number on " The Friendship Day" in August 19, I was told by his cousin that he is no more. We all pray for his soul to rest in peace in eternity
There are other commonly used data sources, including spreadsheets,non-relational databases and even text files (possibly compressed).Open Database Connectivity (ODBC) is a standard to use all ofthese data sources. It originated on Windows (see -us/sql/odbc/microsoft-open-database-connectivity-odbc)but is also implemented on Linux/Unix/macOS.
The first piece of advice is to avoid doing so if possible! If you haveaccess to Excel, export the data you want from Excel in tab-delimited orcomma-separated form, and use read.delim or read.csv toimport it into R. (You may need to use read.delim2 orread.csv2 in a locale that uses comma as the decimal point.)Exporting a DIF file and reading it using read.DIF is anotherpossibility.
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This is the layer responsible for opening and closing communication between the two devices. The time between when the communication is opened and closed is known as the session. The session layer ensures that the session stays open long enough to transfer all the data being exchanged, and then promptly closes the session in order to avoid wasting resources. The session layer also synchronizes data transfer with checkpoints.
The transport layer (also known as layer 4) is responsible for end-to-end communication between the two devices. This includes taking data from the session layer and breaking it up into chunks called segments before sending it to the Network layer (layer 3). It is also responsible for reassembling the segments on the receiving device into data the session layer can consume.
The data link layer is very similar to the network layer, except the data link layer facilitates data transfer between two devices on the same network. The data link layer takes packets from the network layer and breaks them into smaller pieces called frames.
This layer includes the physical equipment involved in the data transfer, such as the cables and switches. This is also the layer where the data gets converted into a bit stream, which is a string of 1s and 0s. The physical layer of both devices must also agree on a signal convention so that the 1s can be distinguished from the 0s on both devices.
A fault-tolerant system has no service interruption but a significantly higher cost, while a highly available system has minimal service interruption. Fault-tolerance requires full hardware redundancy as if the main system fails, with no loss in uptime, another system should take over.
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. It has many similarities with existing distributed file systems.
With the increasing amount of data and high availability requirements, the approach to database design has also changed dramatically. To increase the ability to scale and at the same time be highly available, we move the logic from the database to separate servers. In this way, the database becomes more independent and focused on the actual process of storing data.
On the other hand, planning around BASE limitations can sometimes be a major disadvantage when compared to the simplicity of ACID transactions. A fully ACID database is the perfect fit for use cases where data reliability and consistency are essential.
Microservices are defined as loosely coupled services that can be independently developed, deployed, and maintained. They include their own stack, database, and database model, and communicate with each other through a network. Microservices have become especially popular in hybrid cloud and multi-cloud environments, and they are also widely used in on-premises data centers. If you want to create a microservices application, you can use the CAP theorem to help you determine a database that will best fit your needs.
An Enterprise Service Bus (ESB) is an architectural pattern whereby a centralized software component performs integrations between applications. It performs transformations of data models, handles connectivity, performs message routing, converts communication protocols, and potentially manages the composition of multiple requests. The ESB can make these integrations and transformations available as a service interface for reuse by new applications.
When used with the Event Sourcing pattern, the store of events is the write model and is the official source of information. The read model of a CQRS-based system provides materialized views of the data, typically as highly denormalized views.
Also, sometimes the output of data returned by the microservices to the front end is not in the exact format or filtered as needed by the front end. To solve this issue, the frontend should have some logic to reformat the data, and therefore, we can use BFF to shift some of this logic to the intermediate layer.
gRPC is a modern open-source high-performance Remote Procedure Call (RPC) framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking, authentication and much more.
The registry keeps track of changes to running instances by polling the deployment environment or subscribing to events. When it detects a newly available service instance, it records it in its database. The Service Registry also de-registers terminated service instances.
Recovery Point Objective (RPO) is the maximum acceptable amount of time since the last data recovery point. This determines what is considered an acceptable loss of data between the last recovery point and the interruption of service.
As we discussed, generating a unique key at scale without duplication and collisions can be a bit of a challenge. To solve this problem, we can create a standalone Key Generation Service (KGS) that generates a unique key ahead of time and stores it in a separate database for later use. This approach can make things simple for us.
While 390 GB seems like a lot for this simple use case, it is important to remember this is for the entirety of our service lifetime and the size of the keys database would not increase like our main database.
We will split the data between different services each having ownership over a particular table. Then we can use a relational database such as PostgreSQL or a distributed NoSQL database such as Apache Cassandra for our use case.
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