Best Data Engineering Tools for Amazon Redshift
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Compare the Top Data Engineering Tools that integrate with Amazon Redshift as of September 2025
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This a list of Data Engineering tools that integrate with Amazon Redshift. Use the filters on the left to add additional filters for products that have integrations with Amazon Redshift. View the products that work with Amazon Redshift in the table below.
What are Data Engineering Tools for Amazon Redshift?
Data engineering tools are designed to facilitate the process of preparing and managing large datasets for analysis. These tools support tasks like data extraction, transformation, and loading (ETL), allowing engineers to build efficient data pipelines that move and process data from various sources into storage systems. They help ensure data integrity and quality by providing features for validation, cleansing, and monitoring. Data engineering tools also often include capabilities for automation, scalability, and integration with big data platforms. By streamlining complex workflows, they enable organizations to handle large-scale data operations more efficiently and support advanced analytics and machine learning initiatives. Compare and read user reviews of the best Data Engineering tools for Amazon Redshift currently available using the table below. This list is updated regularly.
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1
dbt
dbt Labs
dbt Labs helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, analysts and engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to reduce data debt, increase trust, and accelerate insights across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.Starting Price: $100 per user per user/ month -
2
Composable DataOps Platform
Composable Analytics
Composable is an enterprise-grade DataOps platform built for business users that want to architect data intelligence solutions and deliver operational data-driven products leveraging disparate data sources, live feeds, and event data regardless of the format or structure of the data. With a modern, intuitive dataflow visual designer, built-in services to facilitate data engineering, and a composable architecture that enables abstraction and integration of any software or analytical approach, Composable is the leading integrated development environment to discover, manage, transform and analyze enterprise data.Starting Price: $8/hr - pay-as-you-go -
3
Peekdata
Peekdata
Consume data from any database, organize it into consistent metrics, and use it with every app. Build your Data and Reporting APIs faster with automated SQL generation, query optimization, access control, consistent metrics definitions, and API design. It takes only days to wrap any data source with a single reference Data API and simplify access to reporting and analytics data across your teams. Make it easy for data engineers and application developers to access the data from any source in a streamlined manner. - The single schema-less Data API endpoint - Review and configure metrics and dimensions in one place via UI - Data model visualization to make faster decisions - Data Export management scheduling AP Ready-to-use Report Builder and JavaScript components for charting libraries (Highcharts, BizCharts, Chart.js, etc.) makes it easy to embed data-rich functionality into your products. And you will not have to make custom report queries anymore!Starting Price: $349 per month -
4
Sifflet
Sifflet
Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset. -
5
RudderStack
RudderStack
RudderStack is the smart customer data pipeline. Easily build pipelines connecting your whole customer data stack, then make them smarter by pulling analysis from your data warehouse to trigger enrichment and activation in customer tools for identity stitching and other advanced use cases. Start building smarter customer data pipelines today.Starting Price: $750/month -
6
Pecan
Pecan AI
Founded in 2018, Pecan is a cutting-edge predictive analytics platform that leverages its pioneering Predictive GenAI technology to eliminate obstacles to AI adoption. Pecan democratizes predictive modeling by enabling data and business teams to harness its power without the need for extensive expertise in data science or data engineering. Guided by Predictive GenAI, the Pecan platform empowers users to rapidly define and train predictive models tailored precisely to their unique business needs. Automated data preparation, model building, and deployment accelerate AI success. Pecan's proprietary fusion of predictive and generative AI quickly delivers meaningful business impact, making AI adoption more accessible, efficient, and impactful than ever before.Starting Price: $950 per month -
7
Datameer
Datameer
Datameer revolutionizes data transformation with a low-code approach, trusted by top global enterprises. Craft, transform, and publish data seamlessly with no code and SQL, simplifying complex data engineering tasks. Empower your data teams to make informed decisions confidently while saving costs and ensuring responsible self-service analytics. Speed up your analytics workflow by transforming datasets to answer ad-hoc questions and support operational dashboards. Empower everyone on your team with our SQL or Drag-and-Drop to transform your data in an intuitive and collaborative workspace. And best of all, everything happens in Snowflake. Datameer is designed and optimized for Snowflake to reduce data movement and increase platform adoption. Some of the problems Datameer solves: - Analytics is not accessible - Drowning in backlog - Long development -
8
Qrvey
Qrvey
Qrvey pioneered multi-tenant self-service analytics for SaaS companies and now leads the evolution toward AI-driven, autonomous analytics. With over 20 years of experience, we provide industry-leading guidance and support, ensuring our clients achieve their analytics goals. Qrvey is the partner of choice for SaaS leaders bringing AI-driven insight to their customers. About Qrvey Platform Qrvey is the embedded analytics platform designed specifically for SaaS companies. Qrvey offers insight, agility and growth. Insight for your customers · True self-service with unlimited customization · AI-driven insights · No-code workflow automation Agility for your product team · End-to-end embedded analytics platform · Native multi-tenant security · Flexible multi-cloud deployments Growth for your business · Flat-rate pricing for scale · Unmatched monetization opportunities · Embedded services -
9
Ascend
Ascend
Ascend gives data teams a unified and automated platform to ingest, transform, and orchestrate their entire data engineering and analytics engineering workloads, 10X faster than ever before. Ascend helps gridlocked teams break through constraints to build, manage, and optimize the increasing number of data workloads required. Backed by DataAware intelligence, Ascend works continuously in the background to guarantee data integrity and optimize data workloads, reducing time spent on maintenance by up to 90%. Build, iterate on, and run data transformations easily with Ascend’s multi-language flex-code interface enabling the use of SQL, Python, Java, and, Scala interchangeably. Quickly view data lineage, data profiles, job and user logs, system health, and other critical workload metrics at a glance. Ascend delivers native connections to a growing library of common data sources with our Flex-Code data connectors.Starting Price: $0.98 per DFC -
10
DQOps
DQOps
DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. The platform provides an efficient user interface to quickly add data sources, configure data quality checks, and manage issues. DQOps comes with over 150 built-in data quality checks, but you can also design custom checks to detect any business-relevant data quality issues. The platform supports incremental data quality monitoring to support analyzing data quality of very big tables. Track data quality KPI scores using our built-in or custom dashboards to show progress in improving data quality to business sponsors. DQOps is DevOps-friendly, allowing you to define data quality definitions in YAML files stored in Git, run data quality checks directly from your data pipelines, or automate any action with a Python Client. DQOps works locally or as a SaaS platform.Starting Price: $499 per month -
11
Decube
Decube
Decube is a data management platform that helps organizations manage their data observability, data catalog, and data governance needs. It provides end-to-end visibility into data and ensures its accuracy, consistency, and trustworthiness. Decube's platform includes data observability, a data catalog, and data governance components that work together to provide a comprehensive solution. The data observability tools enable real-time monitoring and detection of data incidents, while the data catalog provides a centralized repository for data assets, making it easier to manage and govern data usage and access. The data governance tools provide robust access controls, audit reports, and data lineage tracking to demonstrate compliance with regulatory requirements. Decube's platform is customizable and scalable, making it easy for organizations to tailor it to meet their specific data management needs and manage data across different systems, data sources, and departments. -
12
IBM Cognos Analytics acts as your trusted co-pilot for business with the aim of making you smarter, faster, and more confident in your data-driven decisions. IBM Cognos Analytics gives every user — whether data scientist, business analyst or non-IT specialist — more power to perform relevant analysis in a way that ties back to organizational objectives. It shortens each user’s journey from simple to sophisticated analytics, allowing them to harness data to explore the unknown, identify new relationships, get a deeper understanding of outcomes and challenge the status quo. Visualize, analyze and share actionable insights about your data with anyone in your organization with IBM Cognos Analytics.
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13
Querona
YouNeedIT
We make BI & Big Data analytics work easier and faster. Our goal is to empower business users and make always-busy business and heavily loaded BI specialists less dependent on each other when solving data-driven business problems. If you have ever experienced a lack of data you needed, time to consuming report generation or long queue to your BI expert, consider Querona. Querona uses a built-in Big Data engine to handle growing data volumes. Repeatable queries can be cached or calculated in advance. Optimization needs less effort as Querona automatically suggests query improvements. Querona empowers business analysts and data scientists by putting self-service in their hands. They can easily discover and prototype data models, add new data sources, experiment with query optimization and dig in raw data. Less IT is needed. Now users can get live data no matter where it is stored. If databases are too busy to be queried live, Querona will cache the data. -
14
Datakin
Datakin
Instantly reveal the order hidden within your complex data world, and always know exactly where to look for answers. Datakin automatically traces data lineage, showing your entire data ecosystem in a rich visual graph. It clearly illustrates the upstream and downstream relationships for each dataset. The Duration tab summarizes a job’s performance in a Gantt-style chart along with its upstream dependencies, making it easy to find bottlenecks. When you need to pinpoint the exact moment of a breaking change, the Compare tab shows how your jobs and datasets have changed between runs. Sometimes jobs that run successfully produce bad output. The Quality tab surfaces critical data quality metrics, showing how they change over time so anomalies become obvious. Datakin helps you find the root cause of issues quickly – and prevent new ones from occurring.Starting Price: $2 per month -
15
Numbers Station
Numbers Station
Accelerating insights, eliminating barriers for data analysts. Intelligent data stack automation, get insights from your data 10x faster with AI. Pioneered at the Stanford AI lab and now available to your enterprise, intelligence for the modern data stack has arrived. Use natural language to get value from your messy, complex, and siloed data in minutes. Tell your data your desired output, and immediately generate code for execution. Customizable automation of complex data tasks that are specific to your organization and not captured by templated solutions. Empower anyone to securely automate data-intensive workflows on the modern data stack, free data engineers from an endless backlog of requests. Arrive at insights in minutes, not months. Uniquely designed for you, tuned for your organization’s needs. Integrated with upstream and downstream tools, Snowflake, Databricks, Redshift, BigQuery, and more coming, built on dbt. -
16
Chalk
Chalk
Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.Starting Price: Free -
17
Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
18
IBM Databand
IBM
Monitor your data health and pipeline performance. Gain unified visibility for pipelines running on cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. An observability platform purpose built for Data Engineers. Data engineering is only getting more challenging as demands from business stakeholders grow. Databand can help you catch up. More pipelines, more complexity. Data engineers are working with more complex infrastructure than ever and pushing higher speeds of release. It’s harder to understand why a process has failed, why it’s running late, and how changes affect the quality of data outputs. Data consumers are frustrated with inconsistent results, model performance, and delays in data delivery. Not knowing exactly what data is being delivered, or precisely where failures are coming from, leads to persistent lack of trust. Pipeline logs, errors, and data quality metrics are captured and stored in independent, isolated systems. -
19
Feast
Tecton
Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution. -
20
Switchboard
Switchboard
Aggregate disparate data at scale, reliably and accurately, to make better business decisions with Switchboard, a data engineering automation platform driven by business teams. Uncover timely insights and accurate forecasts. No more outdated manual reports and error-prone pivot tables that don’t scale. Directly pull and reconfigure data sources in the right formats in a no-code environment. Reduce your dependency on the engineering team. Automatic monitoring and backfilling make API outages, bad schemas, and missing data a thing of the past. Not a dumb API, but an ecosystem of pre-built connectors that are easily and quickly adapted to actively transform raw data into a strategic asset. Our team of experts has worked in data teams at Google and Facebook. We’ve automated those best practices to elevate your data game. A data engineering automation platform with authoring and workflow processes proven to scale with terabytes of data. -
21
Aggua
Aggua
Aggua is a data fabric augmented AI platform that enables data and business teams Access to their data, creating Trust and giving practical Data Insights, for a more holistic, data-centric decision-making. Instead of wondering what is going on underneath the hood of your organization's data stack, become immediately informed with a few clicks. Get access to data cost insights, data lineage and documentation without needing to take time out of your data engineer's workday. Instead of spending a lot of time tracing what a data type change will break in your data pipelines, tables and infrastructure, with automated lineage, your data architects and engineers can spend less time manually going through logs and DAGs and more time actually making the changes to infrastructure.
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