Alternatives to Google Cloud Composer

Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud. Key Features: Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines. Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance. Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects. Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.

The most accurate, complete, and timely data provides the best insight. Minitab Connect empowers data users across the enterprise with self service tools to transform diverse data into a network of data pipelines that feed analytics initiatives, foster collaboration and foster organizational-wide collaboration. Users can seamlessly combine and explore data from various sources, including databases, on-premise and cloud apps, unstructured data and spreadsheets. Automated workflows make data integration faster and provide powerful data preparation tools that allow for transformative insights. Data integration tools that are intuitive and flexible allow users to connect and blend data from multiple sources such as data warehouses, IoT devices and cloud storage.

DataBahn is an advanced platform that harnesses the power of AI to manage data pipelines and enhance security, streamlining the processes of data collection, integration, and optimization from a variety of sources to various destinations. Boasting a robust array of over 400 connectors, it simplifies the onboarding process and boosts the efficiency of data flow significantly. The platform automates data collection and ingestion, allowing for smooth integration, even when dealing with disparate security tools. Moreover, it optimizes costs related to SIEM and data storage through intelligent, rule-based filtering, which directs less critical data to more affordable storage options. It also ensures real-time visibility and insights by utilizing telemetry health alerts and implementing failover handling, which guarantees the integrity and completeness of data collection. Comprehensive data governance is further supported by AI-driven tagging, automated quarantining of sensitive information, and mechanisms in place to prevent vendor lock-in. In addition, DataBahn's adaptability allows organizations to stay agile and responsive to evolving data management needs.

Fivetran is a comprehensive data integration solution designed to centralize and streamline data movement for organizations of all sizes. With more than 700 pre-built connectors, it effortlessly transfers data from SaaS apps, databases, ERPs, and files into data warehouses and lakes, enabling real-time analytics and AI-driven insights. The platform’s scalable pipelines automatically adapt to growing data volumes and business complexity. Leading companies such as Dropbox, JetBlue, Pfizer, and National Australia Bank rely on Fivetran to reduce data ingestion time from weeks to minutes and improve operational efficiency. Fivetran offers strong security compliance with certifications including SOC 1 & 2, GDPR, HIPAA, ISO 27001, PCI DSS, and HITRUST. Users can programmatically create and manage pipelines through its REST API for seamless extensibility. The platform supports governance features like role-based access controls and integrates with transformation tools like dbt Labs. Fivetran helps organizations innovate by providing reliable, secure, and automated data pipelines tailored to their evolving needs.

Dataform provides a platform for data analysts and engineers to create and manage scalable data transformation pipelines in BigQuery using solely SQL from a single, integrated interface. The open-source core language allows teams to outline table structures, manage dependencies, include column descriptions, and establish data quality checks within a collective code repository, all while adhering to best practices in software development, such as version control, various environments, testing protocols, and comprehensive documentation. A fully managed, serverless orchestration layer seamlessly oversees workflow dependencies, monitors data lineage, and executes SQL pipelines either on demand or on a schedule through tools like Cloud Composer, Workflows, BigQuery Studio, or external services. Within the browser-based development interface, users can receive immediate error notifications, visualize their dependency graphs, link their projects to GitHub or GitLab for version control and code reviews, and initiate high-quality production pipelines in just minutes without exiting BigQuery Studio. This efficiency not only accelerates the development process but also enhances collaboration among team members.

Amazon Managed Workflows for Apache Airflow (MWAA) is a service that simplifies the orchestration of Apache Airflow, allowing users to efficiently establish and manage comprehensive data pipelines in the cloud at scale. Apache Airflow itself is an open-source platform designed for the programmatic creation, scheduling, and oversight of workflows, which are sequences of various processes and tasks. By utilizing Managed Workflows, users can leverage Airflow and Python to design workflows while eliminating the need to handle the complexities of the underlying infrastructure, ensuring scalability, availability, and security. This service adapts its workflow execution capabilities automatically to align with user demands and incorporates AWS security features, facilitating swift and secure data access. Overall, MWAA empowers organizations to focus on their data processes without the burden of infrastructure management.

Airflow is a community-driven platform designed for the programmatic creation, scheduling, and monitoring of workflows. With its modular architecture, Airflow employs a message queue to manage an unlimited number of workers, making it highly scalable. The system is capable of handling complex operations through its ability to define pipelines using Python, facilitating dynamic pipeline generation. This flexibility enables developers to write code that can create pipelines on the fly. Users can easily create custom operators and expand existing libraries, tailoring the abstraction level to meet their specific needs. The pipelines in Airflow are both concise and clear, with built-in parametrization supported by the robust Jinja templating engine. Eliminate the need for complex command-line operations or obscure XML configurations! Instead, leverage standard Python functionalities to construct workflows, incorporating date-time formats for scheduling and utilizing loops for the dynamic generation of tasks. This approach ensures that you retain complete freedom and adaptability when designing your workflows, allowing you to efficiently respond to changing requirements. Additionally, Airflow's user-friendly interface empowers teams to collaboratively refine and optimize their workflow processes.

Open core technology facilitates the integration of hybrid and multi-cloud environments. Built on the open-source initiative CDAP, Data Fusion guarantees portability of data pipelines for its users. The extensive compatibility of CDAP with both on-premises and public cloud services enables Cloud Data Fusion users to eliminate data silos and access previously unreachable insights. Additionally, its seamless integration with Google’s top-tier big data tools enhances the user experience. By leveraging Google Cloud, Data Fusion not only streamlines data security but also ensures that data is readily available for thorough analysis. Whether you are constructing a data lake utilizing Cloud Storage and Dataproc, transferring data into BigQuery for robust data warehousing, or transforming data for placement into a relational database like Cloud Spanner, the integration capabilities of Cloud Data Fusion promote swift and efficient development while allowing for rapid iteration. This comprehensive approach ultimately empowers businesses to derive greater value from their data assets.

Data Flow Manager is an on-premise tool designed to deploy & promote Apache NiFi data flows within minutes - no need for NiFi UI & controller services. Run unlimited NiFi data flows with pay-per-node pricing—no cloud, no CPU limits. Automate everything from NiFi flow deployment to promotion and scheduling. Monitor performance, enforce RBAC, and log every action. DFM even helps you build better NiFi flows with an AI-powered NiFi data flow creation assistant. Backed by 24x7 expert NiFi support and 99.99% uptime, DFM delivers total control and security.

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.

Astera Centerprise offers an all-encompassing on-premise data integration platform that simplifies the processes of extracting, transforming, profiling, cleansing, and integrating data from various sources within a user-friendly drag-and-drop interface. Tailored for the complex data integration requirements of large enterprises, it is employed by numerous Fortune 500 firms, including notable names like Wells Fargo, Xerox, and HP. By leveraging features such as process orchestration, automated workflows, job scheduling, and immediate data preview, businesses can efficiently obtain precise and unified data to support their daily decision-making at a pace that meets the demands of the modern business landscape. Additionally, it empowers organizations to streamline their data operations without the need for extensive coding expertise, making it accessible to a broader range of users.

Optimize your time and reduce expenses by simplifying data pipelines using hassle-free open source solutions. Covering everything from data ingestion to visualization, all components are seamlessly integrated, fully managed, and exceptionally reliable, ensuring your engineering team enjoys working with data. You can opt for any of DoubleCloud’s managed open source services or take advantage of the entire platform's capabilities, which include data storage, orchestration, ELT, and instantaneous visualization. We offer premier open source services such as ClickHouse, Kafka, and Airflow, deployable on platforms like Amazon Web Services or Google Cloud. Our no-code ELT tool enables real-time data synchronization between various systems, providing a fast, serverless solution that integrates effortlessly with your existing setup. With our managed open-source data visualization tools, you can easily create real-time visual representations of your data through interactive charts and dashboards. Ultimately, our platform is crafted to enhance the daily operations of engineers, making their tasks more efficient and enjoyable. This focus on convenience is what sets us apart in the industry.

K2View believes that every enterprise should be able to leverage its data to become as disruptive and agile as possible. We enable this through our Data Product Platform, which creates and manages a trusted dataset for every business entity – on demand, in real time. The dataset is always in sync with its sources, adapts to changes on the fly, and is instantly accessible to any authorized data consumer. We fuel operational use cases, including customer 360, data masking, test data management, data migration, and legacy application modernization – to deliver business outcomes at half the time and cost of other alternatives.

Prefect Cloud serves as a centralized hub for managing your workflows effectively. By deploying from Prefect core, you can immediately obtain comprehensive oversight and control over your operations. The platform features an aesthetically pleasing user interface that allows you to monitor the overall health of your infrastructure effortlessly. You can receive real-time updates and logs, initiate new runs, and access vital information just when you need it. With Prefect's Hybrid Model, your data and code stay on-premises while Prefect Cloud's managed orchestration ensures seamless operation. The Cloud scheduler operates asynchronously, guaranteeing that your tasks commence punctually without fail. Additionally, it offers sophisticated scheduling capabilities that enable you to modify parameter values and define the execution environment for each execution. You can also set up personalized notifications and actions that trigger whenever there are changes in your workflows. Keep track of the status of all agents linked to your cloud account and receive tailored alerts if any agent becomes unresponsive. This level of monitoring empowers teams to proactively tackle issues before they escalate into significant problems.

Streamline the self-service provisioning and refreshing of enterprise workloads while seamlessly integrating with your current toolchain. Enable efficient data delivery and reutilization for data scientists via a comprehensive suite of APIs and automation tools. Achieve data recovery across any cloud environment from any moment in time, concurrently and at scale, surpassing traditional legacy solutions. Reduce the impact of ransomware and cyber threats by ensuring rapid recovery through immutable backup systems. A consolidated platform enhances the protection, security, retention, governance, and recovery of your data, whether on-premises or in the cloud. Actifio’s innovative software platform transforms isolated data silos into interconnected data pipelines. The Virtual Data Pipeline (VDP) provides comprehensive data management capabilities — adaptable for on-premises, hybrid, or multi-cloud setups, featuring extensive application integration, SLA-driven orchestration, flexible data movement, and robust data immutability and security measures. This holistic approach not only optimizes data handling but also empowers organizations to leverage their data assets more effectively.

Unravel empowers data functionality across various environments, whether it’s Azure, AWS, GCP, or your own data center, by enhancing performance, automating issue resolution, and managing expenses effectively. It enables users to oversee, control, and optimize their data pipelines both in the cloud and on-site, facilitating a more consistent performance in the applications that drive business success. With Unravel, you gain a holistic perspective of your complete data ecosystem. The platform aggregates performance metrics from all systems, applications, and platforms across any cloud, employing agentless solutions and machine learning to thoroughly model your data flows from start to finish. This allows for an in-depth exploration, correlation, and analysis of every component within your contemporary data and cloud infrastructure. Unravel's intelligent data model uncovers interdependencies, identifies challenges, and highlights potential improvements, providing insight into how applications and resources are utilized, as well as distinguishing between effective and ineffective elements. Instead of merely tracking performance, you can swiftly identify problems and implement solutions. Utilize AI-enhanced suggestions to automate enhancements, reduce expenses, and strategically prepare for future needs. Ultimately, Unravel not only optimizes your data management strategies but also supports a proactive approach to data-driven decision-making.

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

Y42 is the first fully managed Modern DataOps Cloud for production-ready data pipelines on top of Google BigQuery and Snowflake.

Orchestra serves as a Comprehensive Control Platform for Data and AI Operations, aimed at empowering data teams to effortlessly create, deploy, and oversee workflows. This platform provides a declarative approach that merges coding with a graphical interface, enabling users to develop workflows at a tenfold speed while cutting maintenance efforts by half. Through its real-time metadata aggregation capabilities, Orchestra ensures complete data observability, facilitating proactive alerts and swift recovery from any pipeline issues. It smoothly integrates with a variety of tools such as dbt Core, dbt Cloud, Coalesce, Airbyte, Fivetran, Snowflake, BigQuery, Databricks, and others, ensuring it fits well within existing data infrastructures. With a modular design that accommodates AWS, Azure, and GCP, Orchestra proves to be a flexible option for businesses and growing organizations looking to optimize their data processes and foster confidence in their AI ventures. Additionally, its user-friendly interface and robust connectivity options make it an essential asset for organizations striving to harness the full potential of their data ecosystems.

Qlik Compose for Data Warehouses offers a contemporary solution that streamlines and enhances the process of establishing and managing data warehouses. This tool not only automates the design of the warehouse but also generates ETL code and implements updates swiftly, all while adhering to established best practices and reliable design frameworks. By utilizing Qlik Compose for Data Warehouses, organizations can significantly cut down on the time, expense, and risk associated with BI initiatives, regardless of whether they are deployed on-premises or in the cloud. On the other hand, Qlik Compose for Data Lakes simplifies the creation of analytics-ready datasets by automating data pipeline processes. By handling data ingestion, schema setup, and ongoing updates, companies can achieve a quicker return on investment from their data lake resources, further enhancing their data strategy. Ultimately, these tools empower organizations to maximize their data potential efficiently.

Connect and centralize data. Transform your data landscape into a flexible powerhouse. Data Virtuality is a data integration platform that allows for instant data access, data centralization, and data governance. Logical Data Warehouse combines materialization and virtualization to provide the best performance. For high data quality, governance, and speed-to-market, create your single source data truth by adding a virtual layer to your existing data environment. Hosted on-premises or in the cloud. Data Virtuality offers three modules: Pipes Professional, Pipes Professional, or Logical Data Warehouse. You can cut down on development time up to 80% Access any data in seconds and automate data workflows with SQL. Rapid BI Prototyping allows for a significantly faster time to market. Data quality is essential for consistent, accurate, and complete data. Metadata repositories can be used to improve master data management.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

Data processing that integrates both streaming and batch operations while being serverless, efficient, and budget-friendly. It offers a fully managed service for data processing, ensuring seamless automation in the provisioning and administration of resources. With horizontal autoscaling capabilities, worker resources can be adjusted dynamically to enhance overall resource efficiency. The innovation is driven by the open-source community, particularly through the Apache Beam SDK. This platform guarantees reliable and consistent processing with exactly-once semantics. Dataflow accelerates the development of streaming data pipelines, significantly reducing data latency in the process. By adopting a serverless model, teams can devote their efforts to programming rather than the complexities of managing server clusters, effectively eliminating the operational burdens typically associated with data engineering tasks. Additionally, Dataflow’s automated resource management not only minimizes latency but also optimizes utilization, ensuring that teams can operate with maximum efficiency. Furthermore, this approach promotes a collaborative environment where developers can focus on building robust applications without the distraction of underlying infrastructure concerns.

Experience robust data engineering processes free from the challenges of infrastructure management. By utilizing straightforward, modular Python, you can define intricate streaming, scheduling, and data backfill pipelines with ease. Transition from traditional ETL methods and access your data instantly, regardless of its complexity. Seamlessly blend deep learning and large language models with structured business datasets to enhance decision-making. Improve forecasting accuracy using up-to-date information, eliminate the costs associated with vendor data pre-fetching, and conduct timely queries for online predictions. Test your ideas in Jupyter notebooks before moving them to a live environment. Avoid discrepancies between training and serving data while developing new workflows in mere milliseconds. Monitor all of your data operations in real-time to effortlessly track usage and maintain data integrity. Have full visibility into everything you've processed and the ability to replay data as needed. Easily integrate with existing tools and deploy on your infrastructure, while setting and enforcing withdrawal limits with tailored hold periods. With such capabilities, you can not only enhance productivity but also ensure streamlined operations across your data ecosystem.

Dafne serves as an orchestration and data workflow engine primarily focused on automating data warehouse processes. It streamlines the creation, definition, scheduling, management, and oversight of production workflows and ETLs, enhancing visibility, reliability, and the handling of dependencies, priorities, and internal constraints, ultimately leading to improved service level agreements (SLAs) and overall performance metrics. Additionally, its user-friendly interface enables teams to efficiently adapt to changing data requirements.

It is difficult, costly, and risky to connect systems to scale AI projects. Pandio's cloud native managed solution simplifies data pipelines to harness AI's power. You can access your data from any location at any time to query, analyze, or drive to insight. Big data analytics without the high cost Enable data movement seamlessly. Streaming, queuing, and pub-sub with unparalleled throughput, latency and durability. In less than 30 minutes, you can design, train, deploy, and test machine learning models locally. Accelerate your journey to ML and democratize it across your organization. It doesn't take months or years of disappointment. Pandio's AI driven architecture automatically orchestrates all your models, data and ML tools. Pandio can be integrated with your existing stack to help you accelerate your ML efforts. Orchestrate your messages and models across your organization.

Create a scalable architecture that treats data products as first-class citizens. Automate and repurpose data products. Enable compliance and robust data governance. Control the costs of your data products and pipelines for Snowflake. This global pharmaceutical giant's data product teams can benefit from next-generation analytics using self-service data and analytics infrastructure that includes Snowflake and other tools that use a data mesh approach. The DataOps.live platform allows them to organize and benefit from next generation analytics. DataOps is a unique way for development teams to work together around data in order to achieve rapid results and improve customer service. Data warehousing has never been paired with agility. DataOps is able to change all of this. Governance of data assets is crucial, but it can be a barrier to agility. Dataops enables agility and increases governance. DataOps does not refer to technology; it is a way of thinking.

You can regain control over your data pipelines and instantly deliver value without any errors. DataKitchen™, DataOps platforms automate and coordinate all people, tools and environments within your entire data analytics organization. This includes everything from orchestration, testing and monitoring, development, and deployment. You already have the tools you need. Our platform automates your multi-tool, multienvironment pipelines from data access to value delivery. Add automated tests to every node of your production and development pipelines to catch costly and embarrassing errors before they reach the end user. In minutes, you can create repeatable work environments that allow teams to make changes or experiment without interrupting production. With a click, you can instantly deploy new features to production. Your teams can be freed from the tedious, manual work that hinders innovation.

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.

Gather all your unstructured data to meet your LLM requirements effectively. Datavolo transforms single-use, point-to-point coding into rapid, adaptable, reusable pipelines, allowing you to concentrate on what truly matters—producing exceptional results. As a dataflow infrastructure, Datavolo provides you with a significant competitive advantage. Enjoy swift, unrestricted access to all your data, including the unstructured files essential for LLMs, thereby enhancing your generative AI capabilities. Experience pipelines that expand alongside you, set up in minutes instead of days, without the need for custom coding. You can easily configure sources and destinations at any time, while trust in your data is ensured, as lineage is incorporated into each pipeline. Move beyond single-use pipelines and costly configurations. Leverage your unstructured data to drive AI innovation with Datavolo, which is supported by Apache NiFi and specifically designed for handling unstructured data. With a lifetime of experience, our founders are dedicated to helping organizations maximize their data's potential. This commitment not only empowers businesses but also fosters a culture of data-driven decision-making.

Data integration, backup, management and connectivity. Cloud-based platform that is 100 percent cloud-based. It offers cloud agility and scalability. No manual upgrades or deployment required. There is no coding wizard that can meet the needs of both IT professionals as well as business users without technical skills. Skyvia suites are available in flexible pricing plans that can be customized for any product. To automate workflows, connect your cloud, flat, and on-premise data. Automate data collection from different cloud sources to a database. In just a few clicks, you can transfer your business data between cloud applications. All your cloud data can be protected and kept secure in one location. To connect with multiple OData consumers, you can share data instantly via the REST API. You can query and manage any data via the browser using SQL or the intuitive visual Query Builder.

Creating real-time applications and services involves numerous components that must work seamlessly together, including Kafka, VPC hosting, infrastructure as code, container orchestration, observability, CI/CD processes, persistent storage, databases, and beyond. The Quix platform simplifies this complexity by managing all these elements for you. You simply connect your data and begin your development process—it's that straightforward. There's no need to set up clusters or manage resource configurations. With Quix connectors, you can easily ingest transaction messages from your financial processing systems, whether they are hosted in a virtual private cloud or an on-premises data center. All data is securely encrypted during transit, and it is compressed using G-Zip and Protobuf to enhance both security and efficiency. Additionally, you can utilize machine learning models or rule-based algorithms to identify fraudulent patterns. The platform allows you to generate fraud warning notifications that can be used as troubleshooting tickets or presented on support dashboards for easy monitoring. Ultimately, Quix streamlines the development process, letting you focus on building rather than managing infrastructure.

BDB is an advanced platform for data analytics and business intelligence that excels in extracting valuable insights from your data. It can be implemented both in cloud environments and on-premises. With a unique microservices architecture, it incorporates components for Data Preparation, Predictive Analytics, Pipelines, and Dashboard design, enabling tailored solutions and scalable analytics across various sectors. Thanks to its robust NLP-driven search functionality, users can harness the potential of data seamlessly across desktops, tablets, and mobile devices. BDB offers numerous integrated data connectors, allowing it to interface with a wide array of popular data sources, applications, third-party APIs, IoT devices, and social media platforms in real-time. It facilitates connections to relational databases, big data systems, FTP/SFTP servers, flat files, and web services, effectively managing structured, semi-structured, and unstructured data. Embark on your path to cutting-edge analytics today, and discover the transformative power of BDB for your organization.

Implement sensors tailored for IoT applications and enhance the data collected by integrating it with environmental and control system information. This integration should occur in real-time with enterprise data, facilitating the deployment of predictive algorithms to uncover fresh insights and leverage your data for impactful purposes. Utilize advanced analytics to foresee maintenance issues, gain insights into asset usage, minimize defects, and fine-tune processes. Capitalize on the capabilities of connected devices to provide remote monitoring and diagnostic solutions. Furthermore, use IoT analytics to anticipate safety risks and ensure compliance with regulations, thereby decreasing workplace accidents. Lumada Data Integration allows for the swift creation and expansion of data pipelines, merging information from various sources, including data lakes, warehouses, and devices, while effectively managing data flows across diverse environments. By fostering ecosystems with clients and business associates in multiple sectors, we can hasten digital transformation, ultimately generating new value for society in the process. This collaborative approach not only enhances innovation but also leads to sustainable growth in an increasingly interconnected world.

Discover how to enhance sales growth effortlessly by utilizing automated data pipelines that connect seamlessly to data lakes or cloud storage solutions without the need for coding. This adaptable platform adheres to industry standards, enabling the integration of sales and marketing data to generate automated insights for more intelligent expansion. Eliminate the hassle and costs associated with cumbersome manual data downloads. You’ll always have a clear understanding of your expenses, only paying for the services you actually use. Empower your tools with rapid access to data that is ready for analytics. Our certified developers prioritize security by exclusively working with official APIs. You can quickly initiate data pipelines sourced from widely-used platforms. With pre-built, pre-transformed pipelines at your disposal, you can unlock crucial data from sources like Amazon Vendor Central, Amazon Seller Central, Instagram Stories, Facebook, Amazon Advertising, Google Ads, and more. The processes for data ingestion and transformation require no coding, allowing teams to swiftly and affordably harness the full potential of their data. Your information is consistently safeguarded and securely stored in a reliable, customer-controlled data destination such as Databricks or Amazon Redshift, ensuring peace of mind as you manage your data assets. This streamlined approach not only saves time but also enhances overall operational efficiency.

Implement production-ready change data capture (CDC) systems for high-volume, real-time data replication effortlessly, without writing any code. Experience an enhanced Change Data Capture process with Arcion, which provides automatic schema conversion, comprehensive data replication, and various deployment options. Benefit from Arcion's zero data loss architecture that ensures reliable end-to-end data consistency alongside integrated checkpointing, all without requiring any custom coding. Overcome scalability and performance challenges with a robust, distributed architecture that enables data replication at speeds ten times faster. Minimize DevOps workload through Arcion Cloud, the only fully-managed CDC solution available, featuring autoscaling, high availability, and an intuitive monitoring console. Streamline and standardize your data pipeline architecture while facilitating seamless, zero-downtime migration of workloads from on-premises systems to the cloud. This innovative approach not only enhances efficiency but also significantly reduces the complexity of managing data replication processes.

GlassFlow is an innovative, serverless platform for building event-driven data pipelines, specifically tailored for developers working with Python. It allows users to create real-time data workflows without the complexities associated with traditional infrastructure solutions like Kafka or Flink. Developers can simply write Python functions to specify data transformations, while GlassFlow takes care of the infrastructure, providing benefits such as automatic scaling, low latency, and efficient data retention. The platform seamlessly integrates with a variety of data sources and destinations, including Google Pub/Sub, AWS Kinesis, and OpenAI, utilizing its Python SDK and managed connectors. With a low-code interface, users can rapidly set up and deploy their data pipelines in a matter of minutes. Additionally, GlassFlow includes functionalities such as serverless function execution, real-time API connections, as well as alerting and reprocessing features. This combination of capabilities makes GlassFlow an ideal choice for Python developers looking to streamline the development and management of event-driven data pipelines, ultimately enhancing their productivity and efficiency. As the data landscape continues to evolve, GlassFlow positions itself as a pivotal tool in simplifying data processing workflows.

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.

AWS Data Pipeline is a robust web service designed to facilitate the reliable processing and movement of data across various AWS compute and storage services, as well as from on-premises data sources, according to defined schedules. This service enables you to consistently access data in its storage location, perform large-scale transformations and processing, and seamlessly transfer the outcomes to AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. With AWS Data Pipeline, you can effortlessly construct intricate data processing workflows that are resilient, repeatable, and highly available. You can rest assured knowing that you do not need to manage resource availability, address inter-task dependencies, handle transient failures or timeouts during individual tasks, or set up a failure notification system. Additionally, AWS Data Pipeline provides the capability to access and process data that was previously confined within on-premises data silos, expanding your data processing possibilities significantly. This service ultimately streamlines the data management process and enhances operational efficiency across your organization.

You determine the cluster size, node specifications, and a range of services, while Yandex Data Proc effortlessly sets up and configures Spark, Hadoop clusters, and additional components. Collaboration is enhanced through the use of Zeppelin notebooks and various web applications via a user interface proxy. You maintain complete control over your cluster with root access for every virtual machine. Moreover, you can install your own software and libraries on active clusters without needing to restart them. Yandex Data Proc employs instance groups to automatically adjust computing resources of compute subclusters in response to CPU usage metrics. Additionally, Data Proc facilitates the creation of managed Hive clusters, which helps minimize the risk of failures and data loss due to metadata issues. This service streamlines the process of constructing ETL pipelines and developing models, as well as managing other iterative operations. Furthermore, the Data Proc operator is natively integrated into Apache Airflow, allowing for seamless orchestration of data workflows. This means that users can leverage the full potential of their data processing capabilities with minimal overhead and maximum efficiency.

Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

Data is essential to improve the efficiency, profitability, and experience of streaming video. Datazoom allows video publishers to manage distributed architectures more efficiently by centralizing, standardizing and integrating data in real time. This creates a more powerful data pipeline, improves observability and adaptability, as well as optimizing solutions. Datazoom is a video data platform which continuously gathers data from endpoints such as a CDN or video player through an ecosystem of collectors. Once the data has been gathered, it is normalized with standardized data definitions. The data is then sent via available connectors to analytics platforms such as Google BigQuery, Google Analytics and Splunk. It can be visualized using tools like Looker or Superset. Datazoom is your key for a more efficient and effective data pipeline. Get the data you need right away. Do not wait to get your data if you have an urgent issue.

Talend Pipeline Designer is an intuitive web-based application designed for users to transform raw data into a format suitable for analytics. It allows for the creation of reusable pipelines that can extract, enhance, and modify data from various sources before sending it to selected data warehouses, which can then be used to generate insightful dashboards for your organization. With this tool, you can efficiently build and implement data pipelines in a short amount of time. The user-friendly visual interface enables both design and preview capabilities for batch or streaming processes directly within your web browser. Its architecture is built to scale, supporting the latest advancements in hybrid and multi-cloud environments, while enhancing productivity through real-time development and debugging features. The live preview functionality provides immediate visual feedback, allowing you to diagnose data issues swiftly. Furthermore, you can accelerate decision-making through comprehensive dataset documentation, quality assurance measures, and effective promotion strategies. The platform also includes built-in functions to enhance data quality and streamline the transformation process, making data management an effortless and automated practice. In this way, Talend Pipeline Designer empowers organizations to maintain high data integrity with ease.

BigBI empowers data professionals to create robust big data pipelines in an interactive and efficient manner, all without requiring any programming skills. By harnessing the capabilities of Apache Spark, BigBI offers remarkable benefits such as scalable processing of extensive datasets, achieving speeds that can be up to 100 times faster. Moreover, it facilitates the seamless integration of conventional data sources like SQL and batch files with contemporary data types, which encompass semi-structured formats like JSON, NoSQL databases, Elastic, and Hadoop, as well as unstructured data including text, audio, and video. Additionally, BigBI supports the amalgamation of streaming data, cloud-based information, artificial intelligence/machine learning, and graphical data, making it a comprehensive tool for data management. This versatility allows organizations to leverage diverse data types and sources, enhancing their analytical capabilities significantly.

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.