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01: NoSQL interview Q&As – Key Value, Wide Column, Document & Graph databases

Q1. What are the key differences between SQL & NoSQL databases? A1. Now a days you have a choice which database to use based on your requirements. There are pros & cons for each database. SQL NoSQL (Not Only SQL) SQL databases store structured data & enforces schema (i.e. structure)…

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06: Q51 – Q61 HBase Interview Questions & Answers

Q51. Is HBase a relational database? A51. HBase is not a relational database. It is a NoSQL database. Hbase is a column-oriented (aka columnar) database management system, which runs on top of HDFS (Hadoop Distribute File System). Q52. What does HBase consist of? Why is it called a columnar &…

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10: Q80 – Q87 HBase Schema Design Interview Q&As

Q80. Why is schema design for HBase is different from relational database design? A80. HBase is a columnar NoSQL database. This means no two rows in a table need to have the same columns. In a columnar database table, each row contains cells containing

Values are stored against each…

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InfluxDB interview questions & answers

Q01. What is InfluxDb?
A01. InfluxDB is an open-source time series database. It is a Big Data, NoSQL database that allows for massive scalability, high availability, fast write, and fast read. As a NoSQL, InfluxDB stores time-series data, which has a series of data points over time.

InfluxDB is a resource-intensive database, which means that it requires a significant amount of memory, CPU, and storage to operate efficiently. InfluxDB is a non-relational database, which means that it doesn’t support the same data modelling and querying capabilities as a relational database, such as MySQL or PostgreSQL. InfluxDB is NOT ACID compliant, and it is a distributed database, which means that it provides eventual consistency.

Q02. What is a time series database?
A02. A time series database (i.e. TSDB) is a database optimised for time-stamped or time series data. Time series data are simply measurements or events that are tracked, monitored, downsampled, and aggregated over time. his could be server metrics, application performance monitoring, network data, sensor data, events, clicks, trades in a market, and many other types of analytics data.

An example use case would be an IoT database for Internet of things (IoT) environments where remote devices are continually capturing metrics for analytical purposes. Another example would be Metrics such as memory & CPU utilisation or process/error count can be tracked to monitor behaviour of an applicationl

Q03. What are the other types of time series databases?… Read more ...



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