Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform. 5 HBase Vs. RDBMS Top Functional Differences Here're the differences unlock now. Random Accessing HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database. Database Rules RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties. HBase follows BASE properties and implements complex queries. Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS. Storage From small to medium storage application there is the use of RDBMS that provides the solution with MySQ

This post tells you differences between rdbms and distributed key-value storage. Rdbms is quite  different from key-value storage. RDBMS (Relational Database) You have already used a  r elational  d atabase  m anagement  s ystem — a storage product that's commonly referred to as  RDBMS .  It is basically a structured data. RDBMS systems are fantastically useful to handle moderate data. The BIG challenge is in scaling beyond a single server.  You can't maintain redundant data in rdbms. All the data available on single server. The entire database runs on single server. So when server is down then database may not be available to normal business operations. Outages and server downs are common in this rdbms model of database. Key-Value Database Key-value storage systems often make use of redundancy within hardware resources to prevent outages. This concept is important when you're running thousands of servers because they're bound to suffer hardware bre

Data is now a variety of patterns. Data is now more than just plain text, it can exist in various persistence-storage mechanisms, with Hadoop distributed file system (HDFS) being one of them. The way data is ingested or the sources from which data is ingested affects the way data is stored. On the other hand, how the data is pushed further into the downstream systems or accessed by the data access layer decides how the data is to be stored. Role of RDBMS The need to store huge volumes of data has forced databases to follow new rules of data relationships and integrity that are different from those of relational database management systems (RDBMS). RDBMS follow the ACID rules of atomicity, consistency, isolation, and durability. These rules make the database reliable to any user of the database. However, searching huge volumes of big data and retrieving data from them would take large amounts of time if all the ACID rules were enforced. A typical scenario is when we search f

1) Who is involved in developing NoSQL? Amazon and Google Papers 2) What is NoSQL? You can use NoSQL on non-relational databases. Like columnar databases, by using NoSQL, you can query data from non-relational databases. 3) What are the unique features of NoSQL databases? no relationship between records need Un-structural data store data that individual records do not have a relationship with each other 4) How NoSQL-databases are faster than traditional RDBMS? Stores database on multiple servers, rather than storing the whole database in a single server Adding replicas on other servers, we can retrieve data faster even one of the servers crashes 5) What are the UNIQUE features of NoSQL? Opensource ACID complaint 6) What are the characteristics of a good NoSQL product? High availability: Fault tolerance when a single server goes down Disaster recovery: For when a data center goes down, or more likely, someone digs up a network cable just outside the data center Support: Someone to st

NoSQL and RDBMS or SQL are different from each other. You may ask what is the difference. Below explained in a way that you can understand quickly. 💡Traditional Database A schema is required. All traditional data warehouses using RDBMS to store datamarts. Databases understand SQL language. It has a specific format and rules to interact with traditional databases. Less scalable. It has certain limitations.  Expensive to make the databases as scalable Data should be in a certain format. Data stored in row format. NoSQL database The growing internet usage and involving a number of devices caused to invent databases that have the capability to store any kind of data. More: MongoDB 3.2 fundamentals for Developers-Learn with Exercises NoSQL Special Features The schema is not required. Ability to handle multiple data types. This is the power of NoSQL. NoSQL is much suitable for analytical databases. Since those should be flexible, scalable, and able to store any f

The traditional RDBMS - Since the days of punch cards and magnetic tapes, files have been physically contiguous bytes that are accessed from start (open file) to finish (end-of-file flag = TRUE). Yes, the storage could be split up on a disk and the assorted data pages connected by pointer chains, but it is still the same model. Then the file is broken into records (more physically contiguous bytes), and records are broken into fields (still more physically contiguous bytes). A file is processed in record by record (read/fetch next) or sequentially navigated in terms of a physical storage location (go to end of file, go back/forward n records, follow a pointer chain, etc.). There is no parallelism in this model. There is also an assumption of a physical ordering of the records within the file and an ordering of fields within the records. A lot of time and resources have been spent sorting records to make this access practical; you did not do random access on a magnetic tape and you co

The below list is complex interview questions as part of Hadoop tutorial (part 1 of 4) you can go through these questions quickly. 1. What is BIG DATA? Ans). Big Data is nothing but an assortment of such a huge and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques. 2. Can you give some examples of Big Data? Ans). There are many real-life examples of Big Data! Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade data per day, a jet airline collects 10 terabytes of sensor data for every 30 minutes of flying time. All these are a day to day examples of Big Data! 3. Can you give a detailed overview of the Big Data being generated by Facebook?   Ans). As of December 31, 2012, there are 1.06 billion monthly active users on Facebook and 680 million mobile users. On an average,

Teradata is most popular database among data warehousing projects. You will learn Teradata from any institute you want. My post intention is you just take a look on course contents you need to learn. Course Contents You Need to Look Before You Start Related: Top Data warehousing Interview Questions Introduction to Teradata Data warehousing concepts and SCD’s OLTP & OLAP Teradata Architecture & Physical Design Teradata Vs. Other RDBMS BYNETPEs AMPs Vprocs Clique Cluster Data Storage Data Distribution Data Access Teradata Space Management Fault Tolerance Data Protection Teradata Indexes and Performance Tuning and Optimization UPI – Unique Primary Index NUPI – Non Unique Primary Index USI – Unique Secondary Index NUSI – Non Unique Secondary Index PPI – Partition Primary Index MLPPI – Multilevel Partition Primary Index STJI – Single Table Join Index MTJI – Multi Table Join Index AJI – Aggregate Join Index Global Temporary Tables and Vola

SQL and NoSQL both or two different languages that will be used on different databases. In resolving bigdata analytics NoSQL is most popular. Where as SQL is popular in relational databases. SQL Vs NOSQL Top Differences SQL SQL is structured query language  It was first commercial language used in RDBMS  SQL language is divided into multiple sub elements NoSQL Data is not in one machine or even one network.  Data can be any type public data and private data  Huge volume of data so you cannot put it in one place.  It is uncoordinated in time as well as space.  It is not always nice, structured data that SQL was meant to handle. Also Read RDBMS Vs NoSQL Databases top differences

What is SAP HANA? HANA stands for High-Performance Analytic Appliance. SAP HANA is a combination of hardware and software, and is therefore an appliance. SAP HANA supports column- and row-level storage. We can store and perform analytics on a huge amount of real-time, non-aggregated transactional data. Hence, HANA acts as both a database and a warehousing tool, which helps in making decisions at the right time. Challenges in Traditional RDBMS? There are a few challenges in traditional databases, such as latency, the cost involved, and complexity in accessing databases. Related: SAP HANA jobs and career options What is Architecture of traditional RDBMS? Presentation Layer: This is the top-most layer and allows users to manipulate data so that they can input it for querying. This data input from users is passed on to the database layer through the application layer and the results are passed back to the application layer to implement business logics. The presentation layer

The most important of the philosophies that underpin MongoDB is the notion that one size does not fit all. For many years, traditional SQL databases (MongoDB is a document-orientated database) have been used for storing content of all types. It didn't matter whether the data was a good fit for the relational model (which is used in all RDBMS databases, such as MySQL, PostgresSQL, SQLite, Oracle, MS SQL Server, and so on). The data was stuffed in there, anyway. Purpose Part of the reason for this is that, generally speaking, i t's much easier (and more secure) to read and write to a database than it is to write to a file system. If you pick up any book that teaches PHP (such as PHP for Absolute Beginners (Apress, 2009)) by Jason Lengstorf, you'll probably find that almost right away the database is used to store information, not the file system.  It's just so much easier to do things that way. And while using a database as a storage bin works, developers always

In traditional RDBMS, when a data source is accessed by multi users at single time, then database will go into deadlock state. One of the advantages of a columnar model is that if two or more users want to use a different subset of columns, they do not have to lock out each other.         (Superior benefits for NoSQL Jobs) This design is made easier because of a disk storage method known as RAID (redundant array of independent disks, originally redundant array of inexpensive disks), which combines multiple disk drives into a logical unit. Data is stored in several patterns called levels that have different amounts of redundancy. The idea of the redundancy is that when one drive fails, the other drives can take over. When a replacement disk drive in put in the array, the data is replicated from the other disks in the array and the system is restored. The following are the various levels of RAID: RAID 0 (block-level striping without parity or mirroring) has no (or zero) re

Frequently asked Hadoop interview questions. 1. What is Hadoop? Hadoop is a framework that allows users the power of distributed computing. 2.What is the difference between SQL and Hadoop? SQL is allowed to work with structured data. But SQL is most suitable for legacy technologies. Hadoop is suitable for unstructured data. And, it is well suited for modern technologis. Hadoop 3. What is Hadoop framework? It is distributed network of commodity servers(A server can contain multiple clusters, and a cluster can have multiple nodes) 4. What are 4 properties of Hadoop? Accessible-Hadoop runs on large clusters of commodity machines Robust-An assumption that low commodity machines cause many machine failures. But it handles these tactfully.  Scalable-Hadoop scales linearly to handle larger data by adding more nodes to the cluster.  Simple-Hadoop allows users to quickly write efficient parallel code 5. What kind of data Hadoop needs? Traditional RDBMS having relational