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Ranked by user rating × review volume. See all Data Engineering tools →
Average price: 19 products listed
19 Listings in Data Engineering Available
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Redpanda is a high-performance, Kafka-API-compatible streaming data platform written in C++ that aims to deliver lower latency, simpler operations, and reduced cost compared to running Apache Kafka. With no JVM or ZooKeeper dependencies and a single binary architecture, it is designed to be easier to deploy and operate while remaining compatible with the Kafka ecosystem, so teams can use existing Kafka tools and clients. Redpanda is used by engineering and data teams that want Kafka compatibility with better performance and operational simplicity, whether self-hosted or as Redpanda Cloud. Its efficiency reduces infrastructure cost, its Kafka compatibility eases adoption, and its streaming capabilities support real-time applications and pipelines. For teams seeking a modern, efficient alternative to Kafka, Redpanda is a growing platform.
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Dagster is a data orchestration platform built around the concept of data assets, letting teams define, build, test, and observe the tables, files, and models their pipelines produce rather than just the tasks that run. Its asset-based model, strong local development and testing, lineage, and observability help data teams build reliable, maintainable data platforms, and Dagster Cloud (Dagster+) adds a hosted, scalable control plane. Dagster is used by data engineering and analytics engineering teams that want software-engineering best practices, testability, and asset lineage in their orchestration. Its asset graph makes data dependencies explicit, its integrations connect the modern data stack, and its developer experience emphasizes testing and reuse. For teams building robust, observable data platforms, Dagster is a leading modern orchestrator.
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Confluent is a cloud-native data streaming platform built on Apache Kafka, founded by Kafka's creators, that helps organizations move, process, and react to data in real time. It provides fully managed Kafka, stream processing with Flink and ksqlDB, connectors, schema management, and governance, letting teams build real-time data pipelines and event-driven applications without operating Kafka infrastructure themselves. Confluent is used by enterprises and data teams that need reliable, scalable real-time data streaming for analytics, microservices, and event-driven systems. Its managed service removes Kafka operational burden, its stream processing enables real-time transformations, and its connectors and governance connect and secure data in motion. For organizations building real-time data infrastructure, Confluent is a leading platform.
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Prefect is a Python-native workflow orchestration platform that helps data teams build, run, schedule, and observe data pipelines and workflows with a focus on dynamic, resilient execution. Its framework lets engineers turn Python functions into orchestrated flows with retries, caching, scheduling, and observability, and Prefect Cloud adds a hosted control plane with monitoring, automations, and collaboration, reducing the brittleness of traditional orchestrators. Prefect is used by data engineering and platform teams that want a flexible, developer-friendly orchestrator that fits modern Python data stacks. Its dynamic workflows adapt to runtime conditions, its observability surfaces failures quickly, and its open-source core plus cloud offering suit teams of many sizes. For data teams orchestrating pipelines and jobs, Prefect is a popular modern alternative to legacy schedulers.
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What is Oxylabs? Oxylabs is proxy and scraping software offering a proxy network and web scraping API provider for large-scale public data collection. Founded in 2015 and based in Vilnius, Lithuania, Oxylabs helps data teams and enterprises work more efficiently and achieve better outcomes. Key features of Oxylabs Residential and datacenter proxies Web scraper API AI parsing Datasets Analytics and reporting Integrations with Python, Node.js, Google Sheets and more Who uses Oxylabs? Oxylabs is built for data teams and enterprises. It suits teams that want residential and datacenter proxies without spreadsheets and disconnected tools. Why choose Oxylabs? Compared with alternatives like Bright Data, Oxylabs differentiates on residential and datacenter proxies. Pricing is quote-based and scoped to your usage and team size.
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What is Browse AI? Browse AI is no-code web scraping software offering a no-code tool that trains robots to extract and monitor data from any website. Founded in 2020 and based in Vancouver, British Columbia, Canada, Browse AI helps business users and analysts work more efficiently and achieve better outcomes. Key features of Browse AI Point-and-click robot training Website change monitoring Spreadsheet and API output Prebuilt robots Analytics and reporting Integrations with Python, Node.js, Google Sheets and more Who uses Browse AI? Browse AI is built for business users and analysts. It suits teams that want point-and-click robot training without spreadsheets and disconnected tools. Why choose Browse AI? Compared with alternatives like Octoparse, Browse AI differentiates on point-and-click robot training. Pricing is quote-based and scoped to your usage and team size.
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What is ScrapingBee? ScrapingBee is web scraping API software offering a web scraping API that handles headless browsers, proxies and CAPTCHAs for developers. Founded in 2019 and based in Paris, France, ScrapingBee helps developers scraping at scale work more efficiently and achieve better outcomes. Key features of ScrapingBee Headless browser rendering Rotating proxies AI data extraction Google search API Analytics and reporting Integrations with Python, Node.js, Google Sheets and more Who uses ScrapingBee? ScrapingBee is built for developers scraping at scale. It suits teams that want headless browser rendering without spreadsheets and disconnected tools. Why choose ScrapingBee? Compared with alternatives like Zyte, ScrapingBee differentiates on headless browser rendering. Pricing is quote-based and scoped to your usage and team size.
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What is Apache Airflow? Apache Airflow is workflow orchestration software offering the leading open-source platform for authoring, scheduling and monitoring data workflows as code. Founded in 2015 and based in Community / Open Source, Apache Airflow helps data engineers orchestrating pipelines work more efficiently and achieve better outcomes. Key features of Apache Airflow Workflow orchestration as code Scheduling and dependencies Rich operator ecosystem Monitoring and retries Analytics and reporting Integrations with Python, Kubernetes, AWS and more Who uses Apache Airflow? Apache Airflow is built for data engineers orchestrating pipelines. It suits teams that want workflow orchestration as code without spreadsheets and disconnected tools. Why choose Apache Airflow? Compared with alternatives like Kestra, Apache Airflow differentiates on workflow orchestration as code. Pricing is quote-based and scoped to your usage and team size.
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Y42 is a turnkey data platform that unifies ingestion, transformation, orchestration, and governance on top of your cloud data warehouse, with Git-based version control and a stateful, managed experience. It lets teams build end-to-end data pipelines with dbt-compatible transformations, integrated ingestion, virtual data builds, and observability in one place, aiming to give the productivity of a managed platform while keeping data and compute in the customer's own warehouse. Y42 is used by data teams that want an integrated, managed data platform without assembling and operating many separate tools, while retaining warehouse-native control. Its Git workflows bring software practices to data, its virtual builds enable safe development, and its unified experience reduces tool sprawl. For teams seeking an all-in-one, warehouse-native data platform, Y42 is a modern option.
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What is Octoparse? Octoparse is web scraping software software offering a desktop and cloud web scraping tool with templates and AI auto-detect for non-programmers. Founded in 2016 and based in Shenzhen, China, Octoparse helps marketers and researchers work more efficiently and achieve better outcomes. Key features of Octoparse Visual scraping workflows Templates for popular sites Cloud extraction IP rotation Analytics and reporting Integrations with Python, Node.js, Google Sheets and more Who uses Octoparse? Octoparse is built for marketers and researchers. It suits teams that want visual scraping workflows without spreadsheets and disconnected tools. Why choose Octoparse? Compared with alternatives like Browse AI, Octoparse differentiates on visual scraping workflows. Pricing is quote-based and scoped to your usage and team size.
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What is Kestra? Kestra is event-driven orchestration software offering an open-source, declarative orchestration platform for data and event-driven workflows in YAML. Founded in 2019 and based in Lille, France, Kestra helps data and platform teams orchestrating pipelines work more efficiently and achieve better outcomes. Key features of Kestra Declarative YAML workflows Event-driven orchestration Rich plugin ecosystem Scalable execution Analytics and reporting Integrations with Python, Kubernetes, AWS and more Who uses Kestra? Kestra is built for data and platform teams orchestrating pipelines. It suits teams that want declarative YAML workflows without spreadsheets and disconnected tools. Why choose Kestra? Compared with alternatives like Apache Airflow, Kestra differentiates on declarative YAML workflows. Pricing is quote-based and scoped to your usage and team size.
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What is Bright Data? Bright Data is web data platform software offering a web data platform with proxy networks, scraping APIs and ready datasets. Founded in 2014 and based in Netanya, Israel, Bright Data helps enterprises and AI companies work more efficiently and achieve better outcomes. Key features of Bright Data Residential and datacenter proxies Web scraper APIs Ready-made datasets Scraping browser Analytics and reporting Integrations with Python, Node.js, Google Sheets and more Who uses Bright Data? Bright Data is built for enterprises and AI companies. It suits teams that want residential and datacenter proxies without spreadsheets and disconnected tools. Why choose Bright Data? Compared with alternatives like Oxylabs, Bright Data differentiates on residential and datacenter proxies. Pricing is quote-based and scoped to your usage and team size.
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Data engineering services firms build and manage the data infrastructure, pipelines, warehouses, and platforms, that make an organization's data usable for analytics and AI. This guide explains what data engineering services are, what they deliver, how engagements work, and how to choose the right partner.
Data engineering services firms build and manage the data infrastructure, pipelines, warehouses, and platforms, that make an organization's data usable for analytics and AI. This guide explains what data engineering services are, what they deliver, how engagements work, and how to choose the right partner.
Data engineering services help organizations design, build, and operate the infrastructure that collects, moves, stores, and prepares data for use. Providers build data pipelines, warehouses and lakes, and the modern data stack so that clean, reliable data is available for analytics, reporting, and AI.
Tech stacks
See where data engineering fits in a complete stack, with the other software, AI agents and services each business needs.
The purpose is to turn scattered, messy, siloed data into a trustworthy foundation that the business can actually use. Analytics and AI are only as good as the data behind them, so data engineering, the plumbing that makes data flow reliably, is essential groundwork that requires specialized, scarce expertise.
Organizations engage data engineering partners to build cloud data platforms, migrate and modernize legacy data systems, construct ETL/ELT pipelines, implement data warehouses and lakehouses, ensure data quality and governance, and prepare data foundations for analytics and AI. Engagements range from platform builds to ongoing data-platform management.
Engaging a data engineering services provider typically starts with discovery: the provider learns your goals, current state, and constraints, then proposes a scope, timeline, team, and commercial model. Work is delivered by their specialists against agreed milestones, with regular reporting and reviews.
Engagements are structured as fixed-scope projects, ongoing retainers or managed services, dedicated teams, or staff augmentation, depending on the work. Clear scope, ownership, communication cadence, and success metrics defined up front are what separate a smooth engagement from a difficult one.
A good data engineering services partner brings not just execution capacity but experience, proven methods, and best practices from many similar engagements, accelerating results and helping you avoid the mistakes that in-house teams doing something for the first time often make.
Building reliable pipelines to extract, transform, and load data from many sources. Solid pipelines are the foundation that keeps data flowing accurately and on time.
Designing and building cloud data warehouses and lakehouses (e.g. Snowflake, BigQuery, Databricks). A well-architected warehouse is the central, performant source of truth for analytics.
Architecting the modern data stack, ingestion, transformation, storage, and orchestration. Good architecture determines scalability, cost, and reliability of the whole data platform.
Migrating and modernizing legacy data systems to the cloud. Expert migration avoids the data loss and downtime that derail DIY moves.
Implementing testing, monitoring, lineage, and governance so data is trustworthy. Data quality is what makes analytics and AI reliable rather than misleading.
Preparing and structuring data to power BI, analytics, and AI/ML. Data readiness is the prerequisite for any successful analytics or AI initiative.
A data engineering services provider brings experienced specialists and proven methods you may not have in-house, raising the quality and speed of delivery.
Established teams and repeatable processes let a provider deliver data engineering services work faster than building the capability from scratch internally.
Engaging a provider converts fixed headcount cost into flexible, scalable spend you can dial up or down as needs change.
Outsourcing data engineering services lets your team concentrate on your core business while experts handle specialized work.
Experienced providers have done similar work many times, reducing the execution and delivery risk of doing it alone.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| Project-based engagement | A defined deliverable with a fixed scope and timeline | Any | Clear scope, timeline, and cost | Less flexible if requirements change mid-project |
| Retainer / managed services | Ongoing work and support over time | Any | Continuity, priority access, predictable cost | Requires a sustained relationship and budget |
| Staff augmentation | Adding specialist capacity to your own team | Teams needing extra hands | Flexible capacity under your direction | You manage the work and integration |
| Dedicated team | A full external team run by the provider | Larger or long-running initiatives | Scales quickly with provider-managed delivery | Higher cost; needs clear alignment |
| Advisory / consulting | Strategy, assessment, and expert guidance | Any | High-leverage expertise and direction | Advice still needs execution |
SaaS & Technology: Tech firms build data platforms to power product analytics and AI.
Financial Services: Firms build governed data platforms for analytics, risk, and reporting.
Retail & E-commerce: Retailers unify data across channels for analytics and personalization.
Healthcare: Providers build compliant data platforms for analytics and research.
Manufacturing: Manufacturers integrate operational and IoT data for analytics.
Media & Entertainment: Media firms build data platforms for audience and content analytics.
Logistics: Logistics firms unify operational data for visibility and optimization.
Insurance: Insurers build data foundations for pricing, risk, and analytics.
Enterprise: Large organizations modernize and govern data platforms at scale.
Prioritize providers with a track record in data engineering services for organizations like yours, similar size, industry, and challenges. Ask for case studies and references.
Assess the depth and certifications of the team who will actually do the work, not just the sales team, and confirm they fit your specific needs.
Clear methodology, reporting cadence, and responsive communication are strong predictors of a successful engagement. Evaluate how they run projects.
Review past work and speak with reference clients about quality, reliability, and how the provider handled challenges.
Confirm they offer an engagement model, project, retainer, staff augmentation, or dedicated team, that fits how you want to work, and can flex as needs change.
For work touching sensitive data or systems, verify security practices, certifications, and compliance relevant to your industry.
Understand the pricing model and what's included, and weigh cost against expertise and outcomes rather than choosing on price alone.
AI is reshaping data engineering services, letting providers deliver faster and at lower cost by automating routine work and augmenting their specialists with AI tools.
Leading providers now build AI into their delivery, using it for analysis, drafting, and acceleration, and increasingly help clients adopt AI as part of the engagement.
Clients should ask how a provider uses AI responsibly: what it automates, how quality and confidentiality are maintained, and how it affects cost and timelines.
Expect AI to raise the bar on speed and value in data engineering services. Favor providers that combine real human expertise with AI-enabled delivery and are transparent about how they use it.
Data engineering is the discipline of designing, building, and operating the infrastructure that collects, moves, stores, and prepares data for use. Data engineering services deliver data pipelines (ETL/ELT), data warehouses and lakes, and the modern data stack so that clean, reliable data is available for analytics, reporting, and AI. The purpose is to turn scattered, messy, siloed data into a trustworthy foundation the business can actually use, because analytics and AI are only as good as the data behind them. Data engineering is essentially the plumbing that makes data flow reliably, and it requires specialized, scarce expertise. Organizations engage data engineering partners to build cloud data platforms, modernize legacy systems, construct pipelines, and prepare data foundations for analytics and AI.
A data engineering firm builds and manages the infrastructure that makes your data usable. Typical work includes designing data architecture, building pipelines (ETL/ELT) to move and transform data from many sources, implementing cloud data warehouses or lakehouses (like Snowflake, BigQuery, or Databricks), migrating and modernizing legacy data systems, ensuring data quality and governance through testing and monitoring, and preparing data to power BI, analytics, and AI/ML. Depending on the engagement, they may build a new data platform, modernize an existing one, or manage your data platform ongoing. Their value is deep, scarce expertise in data infrastructure and the modern data stack, delivering a reliable, scalable foundation faster and more soundly than a team doing it for the first time. The result is trustworthy data the business can build analytics and AI on.
Data engineering is the essential foundation for analytics and AI, because both are only as good as the data behind them. Without reliable data engineering, data stays scattered across systems, inconsistent, and untrustworthy, so dashboards conflict, analyses can't be trusted, and AI models fail or produce poor results. Data engineering builds the pipelines, warehouses, and quality controls that deliver clean, consistent, well-structured data where analytics and AI need it. In fact, data preparation is typically the largest effort in any analytics or AI initiative, and poor data foundations are a leading cause of failed AI projects. Investing in data engineering first, getting your data reliable, governed, and accessible, is what makes downstream analytics and AI actually work and deliver value.
Data engineering services are typically priced as scoped projects (for a platform build or migration) or as ongoing monthly engagements (for building and managing a data platform over time), with rates depending on the provider's expertise and the team's seniority and location. A focused pipeline or warehouse implementation costs less than a full data-platform build or a large legacy migration. Beyond the provider's fees, budget for cloud data platform and compute costs (for the warehouse, storage, and processing), which are ongoing. When budgeting, weigh the cost against the value: reliable data infrastructure is the prerequisite for analytics and AI that drive decisions and revenue, and getting it right avoids the far higher cost of failed analytics and AI initiatives built on bad data. Start with a scoped foundation and expand.
The modern data stack is a set of cloud-based, modular tools that together handle the flow of data from source to insight. It typically includes data ingestion tools that pull data from sources, a cloud data warehouse or lakehouse (like Snowflake, BigQuery, or Databricks) as the central store, transformation tools (such as dbt) to clean and model data, orchestration to schedule and manage pipelines, and BI/analytics tools on top. Its advantages over legacy systems are scalability, flexibility, faster implementation, and the ability to mix best-of-breed tools. Data engineering firms architect and build the modern data stack tailored to your needs. When engaging a partner, look for expertise in the specific components you'll use and an architecture designed for your scale, cost targets, and analytics/AI goals.
Data engineering and data science are complementary but distinct. Data engineering builds and manages the infrastructure, pipelines, warehouses, and quality controls, that collects, moves, stores, and prepares data, making reliable, well-structured data available. Data science uses that prepared data to extract insights and build models, performing analysis, statistics, and machine learning to answer questions and make predictions. In short, data engineering makes data usable; data science uses it to create value. The two depend on each other: data scientists can't work effectively without the clean, accessible data that engineers provide, and data engineering exists to serve analytics and data science needs. Many organizations need data engineering first to build the foundation, then data science on top. When hiring services, be clear whether you need infrastructure (engineering) or analysis and modeling (science), or both.
Choose a data engineering partner based on proven, relevant expertise and technical fit. Look for experience building the specific components you need, pipelines, cloud warehouses or lakehouses, migrations, on the platforms you use or plan to use (Snowflake, BigQuery, Databricks, and the modern data stack), and ask for case studies and references from similar projects. Assess the actual engineers' skills and certifications, their approach to data quality, governance, and documentation, and how they'll transfer knowledge so your team can maintain the platform. Confirm they design for scalability and cost efficiency, not just a quick build. Because data infrastructure is foundational and long-lived, prioritize sound architecture and quality practices over the lowest price, verify capability with references, and consider starting with a scoped project to validate the partnership before a larger build.
