AWS Redshift Data Integrations & ETL Tools [Free & Paid]
Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services. Here's the complete guide on everything you need to know about using AWS Redshift data integrations.
Amazon Redshift Overview
What is AWS Redshift?
AWS Redshift is a data warehouse that provides high-performance data processing capabilities for large amounts of data.
Redshift clusters are scalable and easy to manage. This makes them an excellent choice for businesses of all sizes. Since Redshift uses SQL for querying data, it's an easy choice for analysts and developers.
Additionally, Redshift can integrate with data lakes for more comprehensive data analysis. This makes it an exceptional warehousing tool for businesses looking to make data-driven decisions.
Amazon Redshift use cases
Amazon Redshift can handle petabytes of data and large datasets. Therefore, you can use it for a wide range of use cases.
- Redshift can quickly process and analyze large amounts of data, making it well-suited for machine learning applications.
- Amazon's scalability allows businesses to easily add more nodes to their clusters as data volumes grow.
- Real-time data processing is another key strength of AWS Redshift. As a result, Redshift is suitable for timely decision-making based on live data.
Redshift pricing
Redshift is a cost-effective data warehousing service from AWS. It lets users analyze large data sets and works well with other AWS services like Amazon S3. However, its price depends on factors like the number of node clusters, data stored in S3, and data processed by Redshift Spectrum.
Below is a table outlining the pricing plans for Amazon Redshift:
| US East (Ohio) Node Clusters | vCPU | Memory | I/O | Price |
|---|---|---|---|---|
| Dense Compute DC2 | ||||
| dc2.large | 2 | 15 GiB | 0.60 GB/s | $0.25 per Hour |
| dc2.8xlarge | 32 | 244 GiB | 7.50 GB/s | $4.80 per Hour |
| RA3 with Redshift Managed Storage* | ||||
| ra3.xlplus | 4 | 32 GiB | 0.65 GB/s | $1.086 per Hour |
| ra3.4xlarge | 12 | 96 GiB | 2.00 GB/s | $3.26 per Hour |
| ra3.16xlarge | 48 | 384 GiB | 8.00 GB/s | $13.04 per Hour |
Redshift Serverless (Redshift Processing Unit): $0.36 per RPU hour
There may be extra costs for things like:
- Data transfer
- Using AWS Glue Data Catalog
- Data encryption with AWS Key Management Service
Top Data Integrations for Amazon Redshift
1. Portable
Portable is ideal for teams with rare data sources. It comes with 300+ built-in connectors and adds more regularly. Portable also develops new data connectors upon request and maintains them.
The integration platform is among the top ETL tools for Redshift since it supports so many third-party data sources.
Key Features
- Portable offers more than 300 built-in connectors for various data sources that are typically hard to find.
- Portable provides the service of developing and maintaining custom connectors for its users.
- All plans come with premium support for users. Thus users receive high-quality assistance with their data integration needs.
Pricing
Start free with manually triggered syncs and unlimited data volumes.
$200 per month for each scheduled data flow.
2. Segment
Segment offers a single integration point for multiple data sources. This makes it easier to unify and manage data. In addition, it supports real-time data streaming. This feature enables organizations to access and act on data as it's generated. It also offers advanced data governance and security features. This integration ensures data privacy and regulatory compliance.
Key Features
- Segment allows data collection from various sources. Some examples are mobile apps, websites, and cloud applications.
- Segment's data streaming capabilities allow data to be sent to Amazon Redshift in real time. This provides up-to-date analysis capabilities.
- Segment enables customer data management, including tracking user behavior and creating customer profiles.
Pricing
You can start with the free account, which includes 1,000 visitors per mo
The team plan starts at $120/mo, which includes 10,000 visitors/mo
3. Denodo
Denodo is a data virtualization platform. It enables connecting and integrating structured and unstructured data sources with zero data replication.
Business data views can be created by integrating and combining data from multiple sources. These views hide the complexity of back-end technologies from end users.
The virtual data model can be consumed securely using standard SQL and other formats like REST, SOAP, and OData.
Key Features
- Denodo simplifies data integration by allowing easy connection to various data sources. There is no need for data replication.
- It enables the creation of customized data models to fit the end users' requirements.
- Data models can be securely accessed in different formats, such as SQL, REST, SOAP, and OData, making them easy to consume and use.
Pricing
You can start with a free trial.
4. Fivetran
Fivetran is a great data integration tool for Amazon Redshift. It has over 300 connectors for integrating data from different sources. Fivetran supports the following features to simplify the workload of data engineers.
- Automatic schema migrations
- Intelligent schema mapping
Fivetran also offers real-time data syncing, ensuring up-to-date and accurate insights.
Key Features
- Automation of data integration tasks. Some examples are schema drift handling, normalization, deduplication, and data transformation orchestration.
- High reliability with 99.9% uptime and idempotent pipelines. This ensures valuable time is spent on something other than maintenance.
- Scalability with 300+ no-code connectors for quick setup and real-time data movement with low impact to source systems.
Pricing
There are four pricing plans; you can start any plan for free.
5. Talend
Talend is a top-tier data integration tool. Its drag-and-drop interface and pre-built connectors make integration and transformation easy. Talend has excellent data quality and governance features. It also provides real-time integration and can handle large data volumes.
Key Features
- Talend supports data management needs from integration to delivery.
- It is flexible and can be deployed in various environments.
- Talend is trusted, offering clear value and supporting security and compliance needs.
Pricing
There is a free trial and four pricing plans. After that, you need to contact sales to know the cost.
No-Code ETL Connectors for AWS
No-code ETL connectors are a powerful way to load and transform data in AWS. It doesn't require extensive coding or development work.
A lot of data sources support JDBC drivers. Therefore most connectors use JDBC to connect to data sources.
Here are the best ETL tools for Redshift.
1. Portable
Portable is a platform that helps data teams connect various data sources to their data warehouses for analytics. In addition, the company offers custom connectors on demand in a production-rate manner.
This way, data teams don't have to worry about API documentation, infrastructure, error handling, rate limits, and data security. Portable also supports various types of authentication and can connect to any tool, even those that are not popular or well-known.
Key Features
- Portable offers 300+ hard-to-find ETL connectors with the most comprehensive coverage.
- Portable offers hands-on support for users. The support is reliable, extremely intuitive, and super easy to use.
- Portable's very responsive team provides a fast turnaround time for building custom connectors.
Pricing
Start free with Manually triggered syncs.
$200 per month - Each scheduled data flow.
2. Stitch
Stitch is a cloud-based data integration tool. It enables companies to quickly move data from various sources to a destination of their choice. Stitch also enables real-time data ingestion with ETL capabilities.
For data integration, it offers a variety of functions, such as data quality checks, error handling, and alerting. Talend purchased Stitch in 2018, and it is now a component of the Talend Data Fabric platform.
Key Features
- Stitch has pre-built connectors for 130+ popular data sources. These include Amazon S3, Google Analytics, Salesforce, and Shopify.
- Stitch allows you to automatically set up workflows that move data from one system to another based on predefined rules.
- Stitch provides real-time data integration capabilities and has monitoring and alerting capabilities. As a result, you will be notified if there are any issues with your data pipelines.
Pricing
There are three pricing plans: Standard, Advanced, and Premium.
You can get a 2-month free trial when using an annual plan.
3. Informatica
Informatica is a data management and integration software. Businesses can obtain, incorporate, and manage data from different sources using their tools. They offer solutions for data administration, integration, quality, and cataloging.
Finance, healthcare, manufacturing, retail, and telecoms are just a few sectors where Informatica is extensively used.
Key Features
- Informatica offers a wide range of data integration features. Some examples are ETL, data masking, data quality, data replication, and data virtualizations.
- Informatica's ETL tool is widely used for connecting and retrieving data from different data sources.
- Informatica serves as a data-cleaning tool. It allows businesses to identify, correct, or remove corrupt or inaccurate records from a database.
Pricing
There are four pricing plans, out of which two plans are free.
4. Matillion
Matillion is a cloud-based data integration platform. It allows organizations to extract, load, and transform data. Building complicated data integrations is simple for users with little coding expertise. This is because of Matillion's drag-and-drop interface for building data pipelines.
It supports a wide variety of data sources and destinations. Some examples are Amazon Redshift, Snowflake, and Microsoft Azure Synapse. Additionally, Matillion provides pre-made connections and transformations to hasten the integration procedure.
Key Features
- Matillion has an intuitive interface for building complex data pipelines quickly and easily. You don't need extensive coding knowledge.
- Matillion comes with a wide range of pre-built connectors. This is to help users speed up the integration process.
- Matillion provides robust scheduling and orchestration features. This feature allows users to automate the execution of data pipelines.
Pricing
There are four pricing plans.
There is a free plan offering unlimited users.
5. Datacoral
Datacoral is a cloud-based data integration platform. It's designed to help businesses connect and manage their various data sources. These data sources include databases, APIs, and files.
Datacoral has pre-built connectors and tools. They allow users to quickly set up and manage data pipelines, automate data ingestion, and perform data transformations.
The platform also has monitoring and data governance tools. The platform is available on AWS as a managed service. It integrates with several well-known tools for data processing and storage. Some examples are Amazon S3, Redshift, and Snowflake.
Key Features
- Datacoral enables users to connect and integrate their various data sources in real time. This allows businesses to make faster, data-driven decisions.
- Datacoral provides automated data ingestion capabilities.
- Datacoral includes features for data governance and monitoring. Some examples are data lineage tracking and data quality enforcement.
Pricing
You can start with a 30-day free trial.
Preparing Your Cloud Data Warehouse
Before you can start using your cloud data warehousing, you need to prepare it. This involves selecting the right tools, configuring your environment, and optimizing your performance.
Preparing your cloud data warehouse is crucial to ensure it meets your business needs. Doing so will help you make informed, data-driven decisions.
Secure your data
Security is a top priority when it comes to storing and managing data.
Securing your data in the cloud requires taking specific steps. These include setting up AWS Accounts and using IAM to manage access. Configuring Amazon S3 buckets properly and enabling SSL encryption is also crucial for the safety of your data.
By following best practices for data security, you can ensure that your data is safe and protected.
Load data sources
Once you have secured your data, the next step is to load data sources into your cloud data warehouse. This step can involve data ingestion from various sources such as SQL Server, MySQL, or Python.
By loading your data sources into your cloud data warehouse, you can ensure that all your data is in one place and easily accessible.
Storing big data
Storing big data can be challenging, but several options are available in the cloud. You can use Amazon S3 to store large amounts of data. You can also leverage Redshift Spectrum to analyze data directly from Amazon S3.
Alternatively, you can use Amazon Redshift Data to store, query, and analyze large amounts of data quickly and easily.
Automate data pipelines
Automating data pipelines can help streamline managing and analyzing data in the cloud. Setting up workflows that automate data ingestion, processing, and analysis is crucial to saving time and resources. Doing so ensures that your data is always up-to-date and accurate, providing the most relevant insights.
Automating these processes also frees up time for data teams to focus on more strategic tasks.
AWS Redshift Data Warehouse Alternatives
AWS Redshift is a popular data warehousing solution. However, several alternatives are available that better fit your business needs. Exploring these alternatives can help you find the solution for your specific use case.
Amazon Athena
Amazon Athena is a serverless query service that analyzes data in Amazon S3 using standard SQL. It's a cost-effective option for businesses of all sizes, as you only pay for the queries you run.
Athena is an AWS service that integrates seamlessly with other AWS tools and services.
Snowflake
When comparing Snowflake vs Redshift, Snowflake stands out as a popular data warehouse solution with various features and functionality. Snowflake can be used to automate data ingestion, processing, and analysis.
Snowflake connectors work well with Salesforce, Oracle, Microsoft, and many other products. In addition, Snowflake's unique architecture allows for fast and efficient processing of large data sets.
Google BigQuery
Google BigQuery is a cloud-based data warehousing solution. It's designed for handling big data.
With BigQuery, you can store and analyze large datasets quickly and easily. You only pay for what you use, making it a cost-effective option for businesses.
Additionally, BigQuery integrates seamlessly with other Google Cloud Platform tools and services. And if your data needs aren't too tall, its pricing is extremely affordable.
PostgreSQL
PostgreSQL is an open-source relational database management system. It is known for its reliability and performance. While not a dedicated data warehousing solution, it can be optimized for analytical workloads. Its schema allows for flexibility in how data is stored and accessed.
Since PostgreSQL is open-source, it can be customized to suit your business needs. Its pricing is competitive if you already have the on-premises or private cloud infrastructure.
Architecting the Modern Data Stack
Building a modern data stack is vital for businesses. It helps unleash the full potential of their data. By optimizing the stack for specific needs, companies can ensure easy access and usability of data. The result is actionable insights that drive informed decision-making.
Data ingestion --- Low-code integrations
Data ingestion is crucial to a modern data stack. Low-code integrations simplify data transformation and data processing. This stage ensures data is ready for analysis. Streamlining data ingestion simplifies the management and analysis of large data volumes.
Data processing --- Continuous sync
Building a modern data stack involves continuous data processing. Strategies such as APIs, automation, serverless computing resources, and ETL vs. ELT processing can help. These strategies ensure that data is processed and analyzed in real time. The result is always up-to-date and accurate data.
Scalable by design --- data management, cloud data warehouse vs. on-premises data centers)
Scalability is a key consideration when building a modern data stack. It ensures that your data infrastructure can meet your data management needs now and future.
You can always see the argument of cloud data warehouses vs. on-premises data centers.
Data warehouses offer a scalable solution that can grow with your business. However, on-premises data centers can take more resources and capital to scale.
Data-driven business value
A modern data stack yields real-time business intelligence that drives meaningful business value for stakeholders. Implementing an embedded ETL process can improve data processing efficiency and accuracy in the modern data stack.
Data governance is crucial, especially with GDPR/SOC2 certifications regulations. Implementing effective data governance policies ensures data is secure and compliant with regulations.
Finally, businesses can ensure easy access and usability of their data by leveraging Redshift consulting services to optimize the stack for their specific needs.