Amazon Redshift vs HBase

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Amazon Redshift vs HBase: What are the differences?

Key Differences between Amazon Redshift and HBase

Amazon Redshift and HBase are two popular data storage and processing solutions. While they both serve the purpose of storing and managing data, there are several key differences between them.

Data Model: Amazon Redshift is a columnar database that organizes data in columns, making it more suitable for analytical workloads. On the other hand, HBase is a NoSQL database that stores data in key-value pairs, allowing for flexible and dynamic data schema.
Data Replication: Amazon Redshift utilizes replication technology to automatically create and maintain multiple copies of data for durability and high availability. In contrast, HBase uses a distributed architecture to replicate data across multiple nodes in a cluster, ensuring fault tolerance and scalability.
Data Consistency: Amazon Redshift guarantees strong consistency, providing ACID properties for transactions. In contrast, HBase offers eventual consistency, where data may take some time to propagate across all the nodes in the cluster.
Querying Capabilities: Amazon Redshift supports structured query language (SQL) for querying and analyzing data, making it more familiar to data analysts and SQL developers. HBase, on the other hand, supports a limited set of query operations, primarily focused on retrieving data by key.
Scalability: Amazon Redshift is highly scalable and can handle petabytes of data. It allows for automatic scaling of storage and compute resources to accommodate data growth. HBase also offers horizontal scalability, allowing for adding more nodes to the cluster for increased storage capacity and processing power.
Data Access: Amazon Redshift offers JDBC and ODBC drivers, enabling easy integration with various business intelligence tools and data visualization platforms. HBase provides APIs like HBase Thrift, HBase REST, and native Java APIs for accessing and manipulating data.

In summary, Amazon Redshift and HBase differ in their underlying data models, replication mechanisms, consistency guarantees, querying capabilities, scalability options, and data access methods.

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Advice on Amazon Redshift, HBase

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

193k views193k

Comments

Detailed Comparison

Amazon Redshift	HBase
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.	Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' 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, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>	-
Statistics
GitHub Stars -	GitHub Stars 5.5K
GitHub Forks -	GitHub Forks 3.4K
Stacks 1.5K	Stacks 511
Followers 1.4K	Followers 498
Votes 108	Votes 15
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	Pros 9 Performance 5 OLTP 1 Fast Point Queries
Integrations
SQLite MySQL Oracle PL/SQL	No integrations available

What are some alternatives to Amazon Redshift, HBase?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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Amazon Redshift vs HBase: What are the differences?

Key Differences between Amazon Redshift and HBase

Amazon Redshift and HBase are two popular data storage and processing solutions. While they both serve the purpose of storing and managing data, there are several key differences between them.

Data Model: Amazon Redshift is a columnar database that organizes data in columns, making it more suitable for analytical workloads. On the other hand, HBase is a NoSQL database that stores data in key-value pairs, allowing for flexible and dynamic data schema.
Data Replication: Amazon Redshift utilizes replication technology to automatically create and maintain multiple copies of data for durability and high availability. In contrast, HBase uses a distributed architecture to replicate data across multiple nodes in a cluster, ensuring fault tolerance and scalability.
Data Consistency: Amazon Redshift guarantees strong consistency, providing ACID properties for transactions. In contrast, HBase offers eventual consistency, where data may take some time to propagate across all the nodes in the cluster.
Querying Capabilities: Amazon Redshift supports structured query language (SQL) for querying and analyzing data, making it more familiar to data analysts and SQL developers. HBase, on the other hand, supports a limited set of query operations, primarily focused on retrieving data by key.
Scalability: Amazon Redshift is highly scalable and can handle petabytes of data. It allows for automatic scaling of storage and compute resources to accommodate data growth. HBase also offers horizontal scalability, allowing for adding more nodes to the cluster for increased storage capacity and processing power.
Data Access: Amazon Redshift offers JDBC and ODBC drivers, enabling easy integration with various business intelligence tools and data visualization platforms. HBase provides APIs like HBase Thrift, HBase REST, and native Java APIs for accessing and manipulating data.

In summary, Amazon Redshift and HBase differ in their underlying data models, replication mechanisms, consistency guarantees, querying capabilities, scalability options, and data access methods.