Answer:- Apache HBase is used when you need random, realtime read/write access to your Big Data. This project’s goal is the hosting of very large tables — billions of rows X millions of columns — atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google’s Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

Answer:- • Linear and modular scalability. • Strictly consistent reads and writes. • Automatic and configurable sharding of tables • Automatic failover support between RegionServers. • Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables. • Easy to use Java API for client access. • Block cache and Bloom Filters for real-time queries. • Query predicate push down via server side Filters • Thrift gateway and an REST-ful Web service that supports XML, Protobuf, and binary data encoding options • Extensible JRuby-based (JIRB) shell • Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX

Answer:- In HBase 0.96, the project moved to a modular structure. Adjust your project’s dependencies to rely upon the HBase-client module or another module as appropriate, rather than a single JAR. You can model your Maven depency after one of the following, depending on your targeted version of HBase. See Section 3.5, “Upgrading from 0.94.x to 0.96.x” or Section 3.3, “Upgrading from 0.96.x to 0.98.x” for more information. • Maven Dependency for HBase 0.98 org.apache.hbase hbase-client 0.98.5-hadoop2 • Maven Dependency for HBase 0.96 org.apache.hbase hbase-client 0.96.2-hadoop2 • Maven Dependency for HBase 0.94 org.apache.hbase hbase 0.94.3

Answer:- The hierarchy for tables in HBase is as follows: Tables >> Column Families >> Rows Columns >> Cells When a table is created, one or more column families are defined as high-level categories for storing data corresponding to an entry in the table. As is suggested by HBase being “column-oriented”, column family data for all table entries, or rows, are stored together. For a given (row, column family) combination, multiple columns can be written at the time the data is written. Therefore, two rows in an HBase table need not necessarily share the same columns, only column families. For each (row, column-family, column) combination HBase can store multiple cells, with each cell associated with a version, or timestamp corresponding to when the data was written. HBase clients can choose to only read the most recent version of a given cell, or read all versions.

Answer:- Always start with the master log (TODO: Which lines?). Normally it’s just printing the same lines over and over again. If not, then there’s an issue. Google or search-hadoop.com should return some hits for those exceptions you’re seeing. An error rarely comes alone in Apache HBase, usually when something gets screwed up what will follow may be hundreds of exceptions and stack traces coming from all over the place. The best way to approach this type of problem is to walk the log up to where it all began, for example, one trick with RegionServers is that they will print some metrics when aborting so grapping for Dump should get you around the start of the problem. RegionServer suicides are ‘normal’, as this is what they do when something goes wrong. For example, if ulimit and max transfer threads (the two most important initial settings, see [ulimit] and dfs.datanode.max.transfer.threads) aren’t changed, it will make it impossible at some point for DataNodes to create new threads that from the HBase point of view is seen as if HDFS was gone. Think about what would happen if your MySQL database was suddenly unable to access files on your local file system, well it’s the same with HBase and HDFS. Another very common reason to see RegionServers committing seppuku is when they enter prolonged garbage collection pauses that last longer than the default ZooKeeper session timeout. For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Long GC pauses above.

Answer:- Both Cassandra and HBase are NoSQL databases, a term for which you can find numerous definitions. Generally, it means you cannot manipulate the database with SQL. However, Cassandra has implemented CQL (Cassandra Query Language), the syntax of which is obviously modeled after SQL. Both are designed to manage extremely large data sets. HBase documentation proclaims that an HBase database should have hundreds of millions or — even better — billions of rows. Anything less, and you’re advised to stick with an RDBMS. Both are distributed databases, not only in how data is stored but also in how the data can be accessed. Clients can connect to any node in the cluster and access any data. In both Cassandra and HBase, the primary index is the row key, but data is stored on disk such that column family members are kept in close proximity to one another. It is, therefore, important to carefully plan the organization of column families. To keep query performance high, columns with similar access patterns should be placed in the same column family. Cassandra lets you create additional, secondary indexes on column values. This can improve data access in columns whose values have a high level of repetition — such as a column that stores the state field of a customer’s mailing address. HBase lacks built-in support for secondary indexes but offers a number of mechanisms that provide secondary index functionality. These are described in HBase’s online reference guide and on HBase community.

Answer:- Hive can help the SQL savvy to run MapReduce jobs. Since its JDBC compliant, it also integrates with existing SQL-based tools. Running Hive queries could take a while since they go over all of the data in the table by default. Nonetheless, the amount of data can be limited via Hive’s partitioning feature. Partitioning allows running a filter query over data that is stored in separate folders, and only read the data which matches the query. It could be used, for example, to only process files created between certain dates, if the files include the date format as part of their name. HBase works by storing data as key/value. It supports four primary operations: put to add or update rows, scan to retrieve a range of cells, get to return cells for a specified row, and delete to remove rows, columns or column versions from the table. Versioning is available so that previous values of the data can be fetched (the history can be deleted every now and then to clear space via HBase compactions). Although HBase includes tables, a schema is only required for tables and column families, but not for columns, and it includes increment/counter functionality. Hive and HBase are two different Hadoop-based technologies – Hive is an SQL-like engine that runs MapReduce jobs, and HBase is a NoSQL key/value database on Hadoop. But hey, why not use them both? Just like Google can be used for search and Facebook for social networking, Hive can be used for analytical queries while HBase for real-time querying. Data can even be read and written from Hive to HBase and back again.

Answer:- Different versions of HBase require different versions of Hadoop. Consult the table below to find which version of Hadoop you will need: HBase Release Number Hadoop Release Number 0.1.x 0.16.x 0.2.x 0.17.x 0.18.x 0.18.x 0.19.x 0.19.x 0.20.x 0.20.x 0.90.4 (current stable) ??? Releases of Hadoop can be found here. We recommend using the most recent version of Hadoop possible, as it will contain the most bug fixes. Note that HBase-0.2.x can be made to work on Hadoop-0.18.x. HBase-0.2.x ships with Hadoop-0.17.x, so to use Hadoop-0.18.x you must recompile Hadoop-0.18.x, remove the Hadoop-0.17.x jars from HBase, and replace them with the jars from Hadoop-0.18.x. Also note that after HBase-0.2.x, the HBase release numbering schema will change to align with the Hadoop release number on which it depends.

Answer:- • MongoDB • Document • Read • Multi-indexed • Cassandra • Google Bigtable like • Write • Using Key or Scan

Answer:- Cassandra is one of the most favored NoSQL distributed database management systems by Apache. With its open-source technology, Cassandra is efficiently designed to store and manage large volumes of data without any failure. Highly scalable for Big Data models and originally designed by Facebook, Apache Cassandra is written in Java comprising flexible schemas. Apache Cassandra has no single point of failure. There are various types of NoSQL databases, and Cassandra is a hybrid of column-oriented and key–value store database. The keyspace is the outermost container for an application, and the table or column family in Cassandra is the keyspace entity.

Answer:- Unlike traditional or any other database, Apache Cassandra delivers near real-time performance simplifying the work of Developers, Administrators, Data Analysts, and Software Engineers. • Instead of master–slave architecture, Cassandra is established on a peer-to-peer architecture ensuring no failure. • It also assures phenomenal flexibility as it allows the insertion of multiple nodes to any Cassandra cluster in any data center. Further, any client can forward its request to any server. • Cassandra facilitates extensible scalability and can be easily scaled up and scaled down as per the requirements. With a high throughput for read and write operations, this NoSQL application need not be restarted while scaling. • Cassandra is also revered for its strong data replication on nodes capability as it allows data storage at multiple locations enabling users to retrieve data from another location if one node fails. Users have the option to set up the number of replicas they want to create. • Shows brilliant performance when used for massive datasets and thus, the most preferable NoSQL DB by most organizations. • Operates on column-oriented structure and thus, quickens and simplifies the process of slicing. Even data access and retrieval becomes more efficient with column-based data model. • Further, Apache Cassandra supports schema-free/schema-optional data model, which un-necessitate the purpose of showing all the columns required by your application.Find out how Cassandra Versus MongoDB can help you get ahead in your career!

Answer:- Tunable consistency is a phenomenal character that makes Cassandra a favored database choice of Developers, Analysts, and Big data Architects. Consistency refers to the up-to-date and synchronized data rows on all their replicas. Cassandra’s tunable consistency allows users to select the consistency level best suited for their use cases. It supports two consistencies: eventual consistency and strong consistency. The former guarantees consistency when no new updates are made on a given data item, i.e., all accesses return the last updated value eventually. Systems with eventual consistency are known to have achieved replica convergence. For strong consistency, Cassandra supports the following condition: R + W > N where, N – Number of replicas W – Number of nodes that need to agree for a successful write R – Number of nodes that need to agree for a successful read

Answer:- Cassandra performs the write function by applying two commits: first, it writes to a commit log on the disk, and then it commits to an in-memory structure known as memtable. Once the two commits are successful, the write is achieved. Writes are written in the table structure as SSTables (sorted string tables). Cassandra offers faster write performance.

Answer:- DataStax OpsCenter: It is the Internet-based management and monitoring solution for Cassandra cluster and DataStax. It is free to download and includes an additional edition of OpsCenter. SPM primarily administers Cassandra metrics and various OS and JVM metrics. Besides Cassandra, SPM also monitors Hadoop, Spark, Solr, Storm, ZooKeeper, and other Big Data platforms. The main features of SPM include correlation of events and metrics, distributed transaction tracing, creating real-time graphs with zooming, anomaly detection, and heartbeat alerting.