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Dr Neelesh Jain
Instructor and Trainer
Follow me: Youtube/FB : DrNeeleshjain
Big Table, HBase, Dynamo
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
Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
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.
Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
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.
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.
Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
HBase
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.
Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
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.
Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
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.
Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain
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.
Thank you !
Request you kindly Subscribe my
Channel and follow me on
DrNeeleshjain
Stay Tuned
and
Keep Learning

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Big Table, H base, Dynamo, Dynamo DB Lecture

  • 1. Dr Neelesh Jain Instructor and Trainer Follow me: Youtube/FB : DrNeeleshjain 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
  • 3. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain 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.
  • 4. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain 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.
  • 5. 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.
  • 6. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain 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.
  • 8. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain 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.
  • 9. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain 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.
  • 10. Dr. Neelesh Jain 8770193851 Follow me: Youtube/FB : DrNeeleshjain 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.
  • 11. Thank you ! Request you kindly Subscribe my Channel and follow me on DrNeeleshjain Stay Tuned and Keep Learning