Best Data Engineering Tools of 2025

We make BI and Big Data analytics easier and more efficient. Our goal is to empower business users, make BI specialists and always-busy business more independent when solving data-driven business problems. Querona is a solution for those who have ever been frustrated by a lack in data, slow or tedious report generation, or a long queue to their BI specialist. Querona has a built-in Big Data engine that can handle increasing data volumes. Repeatable queries can be stored and calculated in advance. Querona automatically suggests improvements to queries, making optimization easier. Querona empowers data scientists and business analysts by giving them self-service. They can quickly create and prototype data models, add data sources, optimize queries, and dig into raw data. It is possible to use less IT. Users can now access live data regardless of where it is stored. Querona can cache data if databases are too busy to query live.

Mozart Data is the all-in-one modern data platform for consolidating, organizing, and analyzing your data. Set up a modern data stack in an hour, without any engineering. Start getting more out of your data and making data-driven decisions today.

Prophecy allows you to connect with many more people, including data analysts and visual ETL developers. To create your pipelines, all you have to do is click and type a few SQL expressions. You will be creating high-quality, readable code for Spark or Airflow by using the Low-Code Designer. This code is then committed to your Git. Prophecy provides a gem builder that allows you to quickly create and roll out your own Frameworks. Data Quality, Encryption and new Sources are just a few examples. Prophecy offers best practices and infrastructure as managed service - making your life and operations easier! Prophecy makes it easy to create workflows that are high-performance and scale out using the cloud.

No more low-level code or gluing together complex systems. SQL makes it easy to build and deploy pipelines quickly. Data engineering service that allows developers and data engineers to quickly build and deploy data pipelines for data-driven apps. It is easy to connect to and find available data using pre-built connectors for messaging, storage, and database engines. Each connection you make will result in a stream of data to or from the system. You can create your pipelines using SQL with Decodable. Pipelines use streams to send and receive data to and from your connections. Streams can be used to connect pipelines to perform the most difficult processing tasks. To ensure data flows smoothly, monitor your pipelines. Create curated streams that can be used by other teams. To prevent data loss due to system failures, you should establish retention policies for streams. You can monitor real-time performance and health metrics to see if everything is working.

Ascend provides data teams with a unified platform that allows them to ingest and transform their data and create and manage their analytics engineering and data engineering workloads. Ascend is supported by DataAware intelligence. Ascend works in the background to ensure data integrity and optimize data workloads, which can reduce maintenance time by up to 90%. Ascend's multilingual flex-code interface allows you to use SQL, Java, Scala, and Python interchangeably. Quickly view data lineage and data profiles, job logs, system health, system health, and other important workload metrics at a glance. Ascend provides native connections to a growing number of data sources using our Flex-Code data connectors.

Data analysts can now gain insights faster and without any barriers. Intelligent data stack automation, gain insights from your data 10x quicker with AI. Intelligence for the modern data-stack has arrived, a technology that was developed at Stanford's AI lab and is now available to enterprises. Use natural language to extract value from your messy data, complex and siloed in minutes. Tell your data what you want and it will generate code to execute. Automate complex data tasks in a way that is specific to your company and not covered by templated solutions. Automate data-intensive workflows using the modern data stack. Discover insights in minutes and not months. Uniquely designed and tuned to your organization's requirements. Snowflake, Databricks Redshift, BigQuery and more are integrated with dbt.

DataLakeHouse.io Data Sync allows users to replicate and synchronize data from operational systems (on-premises and cloud-based SaaS), into destinations of their choice, primarily Cloud Data Warehouses. DLH.io is a tool for marketing teams, but also for any data team in any size organization. It enables business cases to build single source of truth data repositories such as dimensional warehouses, data vaults 2.0, and machine learning workloads. Use cases include technical and functional examples, including: ELT and ETL, Data Warehouses, Pipelines, Analytics, AI & Machine Learning and Data, Marketing and Sales, Retail and FinTech, Restaurants, Manufacturing, Public Sector and more. DataLakeHouse.io has a mission: to orchestrate the data of every organization, especially those who wish to become data-driven or continue their data-driven strategy journey. DataLakeHouse.io, aka DLH.io, allows hundreds of companies manage their cloud data warehousing solutions.

Data engineering workflows that are powerful, but without the headaches of infrastructure. Simple, reusable Python is used to define complex streaming, scheduling and data backfill pipelines. Fetch all your data in real time, no matter how complicated. Deep learning and LLMs can be used to make decisions along with structured business data. Don't pay vendors for data that you won't use. Instead, query data right before online predictions. Experiment with Jupyter and then deploy into production. Create new data workflows and prevent train-serve skew in milliseconds. Instantly monitor your data workflows and track usage and data quality. You can see everything you have computed, and the data will replay any information. Integrate with your existing tools and deploy it to your own infrastructure. Custom hold times and withdrawal limits can be set.

DatErica: Revolutionizing Data Processing DatErica, a cutting edge data processing platform, automates and streamlines data operations. It provides scalable, flexible solutions to complex data requirements by leveraging a robust technology stack that includes Node.js. The platform provides advanced ETL capabilities and seamless data integration across multiple sources. It also offers secure data warehousing. DatErica’s AI-powered tools allow sophisticated data transformation and verification, ensuring accuracy. Users can make informed decisions with real-time analytics and customizable dashboards. The user-friendly interface simplifies the workflow management while real-time monitoring, alerts and notifications enhance operational efficiency. DatErica is perfect for data engineers, IT teams and businesses that want to optimize their data processes.

Advanced Analytics Software Platform that Helps Sales, Marketing and Technology Leaders Make Great Business Decisions Based On Powerful Data-Driven Information. This software addresses the wide range of AI requirements across data infrastructure, data engineering, and data analytics. Each client's unique requirements are met with customized UI, workflows, and proprietary algorithms. Modular components allow for custom configurations. This component supports, augments, and automates decision-making. Better business results are possible by eliminating human biases. The adoption rate of AI is unprecedented. Leading companies need a rapid and scaleable implementation strategy to stay competitive. These four capabilities can be combined to create a scalable business impact.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Fivetran is the smartest method to replicate data into your warehouse. Our zero-maintenance pipeline is the only one that allows for a quick setup. It takes months of development to create this system. Our connectors connect data from multiple databases and applications to one central location, allowing analysts to gain profound insights into their business.

AI teams are faced with challenges that require new technologies. These technologies are built by us. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. AI and software development go hand in hand. Built with data scientists, ML experts, and data engineers at heart. Don't reinvent your wheel! Production is fast and cost-effective. All your data is stored by you. Your machines are used to train your models. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. New technologies are required for AI teams. These technologies are built by us. Studio is an extension to BitBucket, GitLab, and GitHub. Register for the online SaaS version, or contact us to start an on-premise installation

Bodo's powerful parallel computing engine and powerful compute engine provide efficient execution and effective scaling, even for 10,000+ cores or PBs of data. Bodo makes it easier to develop and maintain data science, data engineering, and ML workloads using standard Python APIs such as Pandas. End-to-end compilation prevents frequent failures and catches errors before they reach production. With Python's simplicity, you can experiment faster with large datasets from your laptop. Produce production-ready code without having to refactor for scaling large infrastructure.

We want ML engineers not to have to worry about data engineering and instead focus on what they are passionate about, building better models in a shorter time. Our product is a powerful framework which makes it 10x faster and easier for developers to explore and understand visual data at scale. Automate the creation of custom interval attributes with pre-trained extractors, or any other model. Custom attributes can be used to visualize data and analyze model performance. You can query, find rare edge cases, and curate training data across your entire data lake using custom attributes. You can easily save, modify, version, comment, and share frames, sequences, or objects with colleagues and third parties. SiaSearch is a data management platform that automatically extracts frame level, contextual metadata and uses it for data exploration, selection, and evaluation. These tasks can be automated with metadata to increase engineering productivity and eliminate the bottleneck in building industrial AI.

You can instantly see the order in your complex data world and know exactly where to find answers. Datakin automatically tracks data lineage and displays your entire data ecosystem as a rich visual graph. It clearly shows the upstream and downstream relationships of each dataset. The Duration tab summarizes the job's performance and its upstream dependencies in a Gantt-style graph. This makes it easy to identify bottlenecks. The Compare tab allows you to see how your jobs and data have changed over time. Sometimes jobs that run well can produce poor output. The Quality tab shows you the most important data quality metrics and how they change over time. This makes anomalies easily visible. Datakin allows you to quickly identify the root cause of problems and prevent them from happening again.

Your offline data can be used to make real-time predictions, without the need for custom pipelines. Data consistency is achieved between offline training and online prediction, eliminating train-serve bias. Standardize data engineering workflows within a consistent framework. Feast is used by teams to build their internal ML platforms. Feast doesn't require dedicated infrastructure to be deployed and managed. Feast reuses existing infrastructure and creates new resources as needed. You don't want a managed solution, and you are happy to manage your own implementation. Feast is supported by engineers who can help with its implementation and management. You are looking to build pipelines that convert raw data into features and integrate with another system. You have specific requirements and want to use an open-source solution.

Datuum is an AI-powered data integration tool that offers a unique solution for organizations looking to streamline their data integration process. With our pre-trained AI engine, Datuum simplifies customer data onboarding by allowing for automated integration from various sources without coding. This reduces data preparation time and helps establish resilient connectors, ultimately freeing up time for organizations to focus on generating insights and improving the customer experience. At Datuum, we have over 40 years of experience in data management and operations, and we've incorporated our expertise into the core of our product. Our platform is designed to address the critical challenges faced by data engineers and managers while being accessible and user-friendly for non-technical specialists. By reducing up to 80% of the time typically spent on data-related tasks, Datuum can help organizations optimize their data management processes and achieve more efficient outcomes.

Kestra is a free, open-source orchestrator based on events that simplifies data operations while improving collaboration between engineers and users. Kestra brings Infrastructure as Code to data pipelines. This allows you to build reliable workflows with confidence. The declarative YAML interface allows anyone who wants to benefit from analytics to participate in the creation of the data pipeline. The UI automatically updates the YAML definition whenever you make changes to a work flow via the UI or an API call. The orchestration logic can be defined in code declaratively, even if certain workflow components are modified.

Roseman Labs allows you to encrypt and link multiple data sets, while protecting the privacy and commercial sensitivity. This allows you combine data sets from multiple parties, analyze them and get the insights that you need to optimize processes. Unlock the potential of your data. Roseman Labs puts the power of encryption at your fingertips with Python's simplicity. Encrypting sensitive information allows you to analyze the data while protecting privacy, commercial sensitivity and adhering GDPR regulations. With enhanced GDPR compliance, you can generate insights from sensitive commercial or personal information. Secure data privacy using the latest encryption. Roseman Labs lets you link data sets from different parties. By analyzing the combined information, you can discover which records are present in multiple data sets. This allows for new patterns to emerge.

Advana, a new-generation data engineering and data-science software, is designed to make implementing and scaling up data analytics easier and faster. This gives you the freedom to focus your attention on what's most important to you: solving your business challenges. Advana offers a variety of data analytics features and capabilities that will allow you to manage, analyze, and transform your data in an efficient and effective manner. Modernize your legacy data analytics solutions. Deliver business value quicker and cheaper by leveraging the no code paradigm. Retain domain experts while computing technology evolves. Collaboration across business functions and IT is seamless with a common interface. You can develop solutions for new technologies without learning new coding. As new technologies become available, you can easily port your solutions.

Ask On Data is an open source Data Engineering/ ETL software that uses chat-based AI. Ask On Data, with its agentic capabilities and next-gen data stack pioneering technology, can help create data pipelines through a simple chat interface. It can be used to perform tasks such as Data Migration, Data Loading and Data Transformations. It also allows for Data Cleaning, Data Wrangling and Data Analysis. Data Scientists can use this tool to get clean data. Data Analysts and BI Engineers can create calculated tables. Data Engineers will also be able to use this tool in order to increase their efficiency.

Xtract Data Automation Suite (XDAS) is a comprehensive platform designed to streamline process automation for data-intensive workflows. It offers a vast library of over 300 pre-built micro solutions and AI agents, enabling businesses to design and orchestrate AI-driven workflows with no code environment, thereby enhancing operational efficiency and accelerating digital transformation. By leveraging these tools, XDAS helps businesses ensure compliance, reduce time to market, enhance data accuracy, and forecast market trends across various industries.

SplineCloud is a knowledge management platform that facilitates the discovery, formalization and exchange of structured, reusable knowledge. It was designed for science and engineering. It allows users to organize their data into structured repositories that are easily accessible and findable. The platform provides tools like an online plot digitizer to extract data from graphs, and an interactive curve-fitting tool that allows users define functional relationships within datasets by using smooth spline function. Users can reuse datasets and relationships in their models and calculation by accessing them directly via the SplineCloud API, or by utilizing client libraries open-source for Python and MATLAB. The platform enables the development of reusable engineering applications and analytical applications. It aims to reduce redundancy and improve decision-making by preserving expert knowledge and reducing redundant design processes.

AtScale accelerates and simplifies business intelligence. This results in better business decisions and a faster time to insight. Reduce repetitive data engineering tasks such as maintaining, curating, and delivering data for analysis. To ensure consistent KPI reporting across BI tools, you can define business definitions in one place. You can speed up the time it takes to gain insight from data and also manage cloud compute costs efficiently. No matter where your data is located, you can leverage existing data security policies to perform data analytics. AtScale's Insights models and workbooks allow you to perform Cloud OLAP multidimensional analysis using data sets from multiple providers - without any data prep or engineering. To help you quickly gain insights that you can use to make business decisions, we provide easy-to-use dimensions and measures.