Amazon Redshift vs Amazon Redshift Spectrum

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

Amazon Redshift Spectrum

Stacks99

Followers147

Votes3

Amazon Redshift vs Amazon Redshift Spectrum: What are the differences?

Introduction:

Here, we will discuss the key differences between Amazon Redshift and Amazon Redshift Spectrum. Both services are offered by Amazon Web Services (AWS) and are designed to handle and analyze large datasets efficiently. However, there are distinct differences in their features and functionalities.

Data Storage and Processing: In Amazon Redshift, data is stored and processed within the Redshift cluster itself. It offers a high-performance columnar data store optimized for online analytic processing (OLAP). On the other hand, Amazon Redshift Spectrum separates storage and processing. It allows users to directly query data stored in Amazon S3 using the same SQL syntax used for Redshift. This feature enables querying and analyzing data without first loading it into Redshift, offering greater flexibility and cost optimization.
Cost Structure: When using Amazon Redshift, users incur costs based on the size of their cluster, regardless of the amount of data stored in it. This means that even if a cluster contains only small amounts of data, the cost is calculated based on the provisioned cluster size. In contrast, Amazon Redshift Spectrum follows a pay-per-use pricing model. Users are billed based on the amount of data scanned from S3 during query execution. This allows organizations to efficiently store and access large volumes of data without incurring unnecessary costs for idle clusters.
Scaling: While both services provide scalability, they differ in their approach. With Amazon Redshift, scaling is achieved by adding more nodes to the Redshift cluster. This vertical scaling technique requires downtime during the scaling process and may cause temporary service disruptions. In contrast, Redshift Spectrum leverages the scalability of Amazon S3. As data is stored in S3, there are no capacity constraints. Users can parallelize queries across thousands of instances, providing seamless scaling without any impact on query performance.
Query Performance: Amazon Redshift is optimized for high-performance OLAP workloads, with data stored on local disks of the cluster nodes. As a result, it offers faster query execution times compared to Redshift Spectrum, especially for frequently accessed and aggregated data. Redshift Spectrum, on the other hand, offloads query processing to Amazon S3, which introduces some latency due to network transfer. Although Redshift Spectrum supports the use of columnar data formats like Parquet and ORC that improve query performance, it may not match the performance of Redshift for real-time interactive queries.
Complex Transformations: Amazon Redshift provides a variety of transformation capabilities, such as joins, aggregations, and complex SQL functions. Users can perform complex analytical operations directly on the data within the Redshift cluster. Redshift Spectrum, while supporting a subset of SQL functions, doesn't provide in-cluster transformations. It primarily focuses on querying the data stored in Amazon S3, which limits the complex transformations that can be performed. Complex transformation operations would require data to be loaded into Redshift for processing.
Data Source: Amazon Redshift requires that the data being queried or analyzed be loaded into the Redshift cluster. It may require data loading using the COPY command or other ETL methods before it can be accessed and analyzed. In contrast, Redshift Spectrum allows querying data directly from Amazon S3. This means that data stored in different formats and sources can be queried without the need for loading or transforming it into the Redshift cluster.

In summary, Amazon Redshift is a fully managed data warehousing service optimized for high-performance OLAP workloads, providing faster query execution times. On the other hand, Amazon Redshift Spectrum offers the ability to directly query data stored in Amazon S3, providing cost optimization, flexibility, and scalability without the need for data loading into the Redshift cluster.

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Advice on Amazon Redshift, Amazon Redshift Spectrum

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.

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Detailed Comparison

Amazon Redshift	Amazon Redshift Spectrum
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.	With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
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
Stacks 1.5K	Stacks 99
Followers 1.4K	Followers 147
Votes 108	Votes 3
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	Pros 1 Great Documentation 1 Good Performance 1 Economical
Integrations
SQLite MySQL Oracle PL/SQL	Amazon S3

What are some alternatives to Amazon Redshift, Amazon Redshift Spectrum?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Related Comparisons

Amazon Redshift vs Amazon Redshift Spectrum: What are the differences?

Introduction:

Data Storage and Processing: In Amazon Redshift, data is stored and processed within the Redshift cluster itself. It offers a high-performance columnar data store optimized for online analytic processing (OLAP). On the other hand, Amazon Redshift Spectrum separates storage and processing. It allows users to directly query data stored in Amazon S3 using the same SQL syntax used for Redshift. This feature enables querying and analyzing data without first loading it into Redshift, offering greater flexibility and cost optimization.
Cost Structure: When using Amazon Redshift, users incur costs based on the size of their cluster, regardless of the amount of data stored in it. This means that even if a cluster contains only small amounts of data, the cost is calculated based on the provisioned cluster size. In contrast, Amazon Redshift Spectrum follows a pay-per-use pricing model. Users are billed based on the amount of data scanned from S3 during query execution. This allows organizations to efficiently store and access large volumes of data without incurring unnecessary costs for idle clusters.
Scaling: While both services provide scalability, they differ in their approach. With Amazon Redshift, scaling is achieved by adding more nodes to the Redshift cluster. This vertical scaling technique requires downtime during the scaling process and may cause temporary service disruptions. In contrast, Redshift Spectrum leverages the scalability of Amazon S3. As data is stored in S3, there are no capacity constraints. Users can parallelize queries across thousands of instances, providing seamless scaling without any impact on query performance.
Query Performance: Amazon Redshift is optimized for high-performance OLAP workloads, with data stored on local disks of the cluster nodes. As a result, it offers faster query execution times compared to Redshift Spectrum, especially for frequently accessed and aggregated data. Redshift Spectrum, on the other hand, offloads query processing to Amazon S3, which introduces some latency due to network transfer. Although Redshift Spectrum supports the use of columnar data formats like Parquet and ORC that improve query performance, it may not match the performance of Redshift for real-time interactive queries.
Complex Transformations: Amazon Redshift provides a variety of transformation capabilities, such as joins, aggregations, and complex SQL functions. Users can perform complex analytical operations directly on the data within the Redshift cluster. Redshift Spectrum, while supporting a subset of SQL functions, doesn't provide in-cluster transformations. It primarily focuses on querying the data stored in Amazon S3, which limits the complex transformations that can be performed. Complex transformation operations would require data to be loaded into Redshift for processing.
Data Source: Amazon Redshift requires that the data being queried or analyzed be loaded into the Redshift cluster. It may require data loading using the COPY command or other ETL methods before it can be accessed and analyzed. In contrast, Redshift Spectrum allows querying data directly from Amazon S3. This means that data stored in different formats and sources can be queried without the need for loading or transforming it into the Redshift cluster.

Amazon Redshift vs Amazon Redshift Spectrum

Overview