Best Data Pipeline Software for Windows of 2024

Find and compare the best Data Pipeline software for Windows in 2024

Use the comparison tool below to compare the top Data Pipeline software for Windows on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    QuerySurge Reviews
    Top Pick
    QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed
  • 2
    Gathr.ai Reviews
    Gathr is a Data+AI fabric, helping enterprises rapidly deliver production-ready data and AI products. Data+AI fabric enables teams to effortlessly acquire, process, and harness data, leverage AI services to generate intelligence, and build consumer applications— all with unparalleled speed, scale, and confidence. Gathr’s self-service, AI-assisted, and collaborative approach enables data and AI leaders to achieve massive productivity gains by empowering their existing teams to deliver more valuable work in less time. With complete ownership and control over data and AI, flexibility and agility to experiment and innovate on an ongoing basis, and proven reliable performance at real-world scale, Gathr allows them to confidently accelerate POVs to production. Additionally, Gathr supports both cloud and air-gapped deployments, making it the ideal choice for diverse enterprise needs. Gathr, recognized by leading analysts like Gartner and Forrester, is a go-to-partner for Fortune 500 companies, such as United, Kroger, Philips, Truist, and many others.
  • 3
    Apache Kafka Reviews

    Apache Kafka

    The Apache Software Foundation

    1 Rating
    Apache Kafka®, is an open-source distributed streaming platform.
  • 4
    CloverDX Reviews

    CloverDX

    CloverDX

    $5000.00/one-time
    2 Ratings
    In a developer-friendly visual editor, you can design, debug, run, and troubleshoot data jobflows and data transformations. You can orchestrate data tasks that require a specific sequence and organize multiple systems using the transparency of visual workflows. Easy deployment of data workloads into an enterprise runtime environment. Cloud or on-premise. Data can be made available to applications, people, and storage through a single platform. You can manage all your data workloads and related processes from one platform. No task is too difficult. CloverDX was built on years of experience in large enterprise projects. Open architecture that is user-friendly and flexible allows you to package and hide complexity for developers. You can manage the entire lifecycle for a data pipeline, from design, deployment, evolution, and testing. Our in-house customer success teams will help you get things done quickly.
  • 5
    Dagster+ Reviews

    Dagster+

    Dagster Labs

    $0
    Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.
  • 6
    Dataplane Reviews
    Dataplane's goal is to make it faster and easier to create a data mesh. It has robust data pipelines and automated workflows that can be used by businesses and teams of any size. Dataplane is more user-friendly and places a greater emphasis on performance, security, resilience, and scaling.
  • 7
    Astera Centerprise Reviews
    Astera Centerprise, a complete on-premise data management solution, helps to extract, transform profile, cleanse, clean, and integrate data from different sources in a code-free, drag and drop environment. This software is specifically designed for enterprise-level data integration and is used by Fortune 500 companies like Wells Fargo and Xerox, HP, as well as other large corporations such as Xerox, HP, HP, and many others. Enterprises can quickly access accurate, consolidated data to support their day-today decision-making at lightning speed through process orchestration, workflow automation and job scheduling.
  • 8
    DPR Reviews

    DPR

    Qvikly

    $50 per user per year
    Data Prep Runner by QVIKPREP simplifies and streamlines data processing. You can improve your business processes, compare data and enhance data profiling. Preparing data for operational reporting, data analytics, and data movement between systems can save you time. Data profiling can help you catch problems early and reduce risk in data integration projects. Automating data processing can increase productivity in operations teams. Data prep is easy and you can build a robust data pipeline. DPR allows you to run checks based on historical data to improve accuracy. Transform transactions into your systems and use data for data-driven test automation. DPR ensures data gets to the right place. Ensure data integration projects deliver on time. Instead of waiting for test cycles, uncover and address data issues before they become a problem. Validate your data using rules and repair data within the data pipeline. DPR makes it easy to compare data between sources with color-coded reports.
  • 9
    Nextflow Reviews

    Nextflow

    Seqera Labs

    Free
    Data-driven computational pipelines. Nextflow allows for reproducible and scalable scientific workflows by using software containers. It allows adaptation of scripts written in most common scripting languages. Fluent DSL makes it easy to implement and deploy complex reactive and parallel workflows on clusters and clouds. Nextflow was built on the belief that Linux is the lingua Franca of data science. Nextflow makes it easier to create a computational pipeline that can be used to combine many tasks. You can reuse existing scripts and tools. Additionally, you don't have to learn a new language to use Nextflow. Nextflow supports Docker, Singularity and other containers technology. This, together with integration of the GitHub Code-sharing Platform, allows you write self-contained pipes, manage versions, reproduce any configuration quickly, and allow you to integrate the GitHub code-sharing portal. Nextflow acts as an abstraction layer between the logic of your pipeline and its execution layer.
  • 10
    Kestra Reviews
    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.
  • 11
    Qlik Compose Reviews
    Qlik Compose for Data Warehouses offers a modern approach to data warehouse creation and operations by automating and optimising the process. Qlik Compose automates the design of the warehouse, generates ETL code and quickly applies updates, all while leveraging best practices. Qlik Compose for Data Warehouses reduces time, cost, and risk for BI projects whether they are on-premises, or in the cloud. Qlik Compose for Data Lakes automates data pipelines, resulting in analytics-ready data. By automating data ingestion and schema creation, as well as continual updates, organizations can realize a faster return on their existing data lakes investments.
  • 12
    CData Sync Reviews
    CData Sync is a universal database pipeline that automates continuous replication between hundreds SaaS applications & cloud-based data sources. It also supports any major data warehouse or database, whether it's on-premise or cloud. Replicate data from hundreds cloud data sources to popular databases destinations such as SQL Server and Redshift, S3, Snowflake and BigQuery. It is simple to set up replication: log in, select the data tables you wish to replicate, then select a replication period. It's done. CData Sync extracts data iteratively. It has minimal impact on operational systems. CData Sync only queries and updates data that has been updated or added since the last update. CData Sync allows for maximum flexibility in partial and full replication scenarios. It ensures that critical data is safely stored in your database of choice. Get a 30-day trial of the Sync app for free or request more information at www.cdata.com/sync
  • 13
    BigBI Reviews
    BigBI allows data specialists to create their own powerful Big Data pipelines interactively and efficiently, without coding! BigBI unleashes Apache Spark's power, enabling: Scalable processing of Big Data (upto 100X faster). Integration of traditional data (SQL and batch files) with new data Sources include semi-structured data (JSON, NoSQL DBs and Hadoop) as well as unstructured data (text, audio, video). Integration of streaming data and cloud data, AI/ML graphs & graphs
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