BigTable, H Base, DynamoDB, Advantages and Disadvantages of BigTable, H Base and DynamoDB

1. Dr Neelesh Jain
Instructor and Trainer
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Big Table, HBase, Dynamo

2. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
Big Table
Cloud Bigtable is a sparsely populated table that can scale to billions of
rows and thousands of columns, enabling you to store terabytes or even
petabytes of data. A single value in each row is indexed; this value is
known as the row key. Cloud Bigtable is ideal for storing very large
amounts of single-keyed data with very low latency. It supports high read
and write throughput at low latency, and it is an ideal data source for
MapReduce operations.
Cloud Bigtable is exposed to applications through multiple client libraries,
including a supported extension to the Apache HBase library for Java. As
a result, it integrates with the existing Apache ecosystem of open-source
Big Data software

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Big Table Advantages
Cloud Bigtable's offers several key advantages
Incredible scalability. Cloud Bigtable scales in direct proportion to the number of
machines in your cluster. A self-managed HBase installation has a design bottleneck
that limits the performance after a certain threshold is reached. Cloud Bigtable does
not have this bottleneck, so you can scale your cluster up to handle more reads and
writes.
Simple administration. Cloud Bigtable handles upgrades and restarts transparently,
and it automatically maintains high data durability. To replicate your data, simply add a
second cluster to your instance, and replication starts automatically.
Cluster resizing without downtime. We can increase the size of a Cloud Bigtable
cluster for a few hours to handle a large load, then reduce the cluster's size again—all
without any downtime. After you change a cluster's size, it takes just a few minutes to
balance performance across all of the nodes in your cluster.

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Big Table Applications
Big Table is very useful for the following applications
● Time-series data, such as CPU and memory usage over time for
multiple servers.
● Marketing data, such as purchase histories and customer
preferences.
● Financial data, such as transaction histories, stock prices, and
currency exchange rates.
● Internet of Things data, such as usage reports from energy meters
and home appliances.
● Graph data, such as information about how users are connected to
one another.

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HBase
● HBase is a distributed column-oriented database built on top of the
Hadoop file system. It is an open-source project and is horizontally
scalable.
● HBase is a data model that is similar to Google’s big table designed to
provide quick random access to huge amounts of structured data. It
leverages the fault tolerance provided by the Hadoop File System
(HDFS).
● It is a part of the Hadoop ecosystem that provides random real-time
read/write access to data in the Hadoop File System.
● One can store the data in HDFS either directly or through HBase.
Data consumer reads/accesses the data in HDFS randomly using
HBase. HBase sits on top of the Hadoop File System and provides
read and write access.

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HBase

7. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
HBase
● HBase is a distributed column-oriented database built on top of the
Hadoop file system. It is an open-source project and is horizontally
scalable.
● HBase is a data model that is similar to Google’s big table designed to
provide quick random access to huge amounts of structured data. It
leverages the fault tolerance provided by the Hadoop File System
(HDFS).
● It is a part of the Hadoop ecosystem that provides random real-time
read/write access to data in the Hadoop File System.
● One can store the data in HDFS either directly or through HBase.
Data consumer reads/accesses the data in HDFS randomly using
HBase. HBase sits on top of the Hadoop File System and provides
read and write access.

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Dynamo
Amazon DynamoDB is a fully managed NoSQL database service
that allows to create database tables that can store and retrieve
any amount of data. It automatically manages the data traffic of
tables over multiple servers and maintains performance. It also
relieves the customers from the burden of operating and scaling a
distributed database. Hence, hardware provisioning, setup,
configuration, replication, software patching, cluster scaling, etc. is
managed by Amazon.

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Benefits of Dynamo
Managed service − Amazon DynamoDB is a managed service. There is no
need to hire experts to manage NoSQL installation. Developers need not worry
about setting up, configuring a distributed database cluster, managing ongoing
cluster operations, etc. It handles all the complexities of scaling, partitions and
re-partitions data over more machine resources to meet I/O performance
requirements.
Scalable − Amazon DynamoDB is designed to scale. There is no need to worry
about predefined limits to the amount of data each table can store. Any amount
of data can be stored and retrieved. DynamoDB will spread automatically with
the amount of data stored as the table grows.

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Benefits of Dynamo
Fast − Amazon DynamoDB provides high throughput at very low latency. As
datasets grow, latencies remain stable due to the distributed nature of
DynamoDB's data placement and request routing algorithms.
Durable and highly available − Amazon DynamoDB replicates data over at least
3 different data centers’ results. The system operates and serves data even
under various failure conditions.
Flexible: Amazon DynamoDB allows creation of dynamic tables, i.e. the table
can have any number of attributes, including multi-valued attributes.
Cost-effective: Payment is for what we use without any minimum charges. Its
pricing structure is simple and easy to calculate.

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