Alternatives to Oarkflow

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

Alooma allows data teams visibility and control. It connects data from all your data silos into BigQuery in real-time. You can set up and flow data in minutes. Or, you can customize, enrich, or transform data before it hits the data warehouse. Never lose an event. Alooma's safety nets make it easy to handle errors without affecting your pipeline. Alooma infrastructure can handle any number of data sources, low or high volume.

AWS Data Pipeline, a web service, allows you to reliably process and transfer data between different AWS compute- and storage services as well as on premises data sources at specific intervals. AWS Data Pipeline allows you to access your data wherever it is stored, transform it and process it at scale, then transfer it to AWS services like Amazon S3, Amazon RDS and Amazon DynamoDB. AWS Data Pipeline makes it easy to create complex data processing workloads that can be fault-tolerant, repeatable, high-availability, and reliable. You don't need to worry about resource availability, managing intertask dependencies, retrying transient errors or timeouts in individual task, or creating a fail notification system. AWS Data Pipeline allows you to move and process data previously stored in on-premises silos.

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.

You can centralize offline data, import files, clean up data, and process it. With one click, export to a live database Streamline data workflows. Your team can access offline data by centralizing it. Dropbase can import offline files. Multiple formats. You can do it however you want. Data can be processed and formatted. Steps for adding, editing, reordering, and deleting data. 1-click exports. Export to database, endpoints or download code in just one click Instant REST API access. Securely query Dropbase data with REST API access keys. You can access data wherever you need it. Combine and process data to create the desired format. No code. Use a spreadsheet interface to process your data pipelines. Each step is tracked. Flexible. You can use a pre-built library of processing functions. You can also create your own. 1-click exports. Export to a database or generate endpoints in just one click Manage databases. Manage databases and credentials.

Panoply makes it easy to store, sync and access all your business information in the cloud. With built-in integrations to all major CRMs and file systems, building a single source of truth for your data has never been easier. Panoply is quick to set up and requires no ongoing maintenance. It also offers award-winning support, and a plan to fit any need.

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.

BDB is a modern data analysis and BI platform that can dig deep into your data to uncover actionable insights. It can be deployed on-premise or in the cloud. Our unique microservices-based architecture includes elements such as Data Preparation and Predictive, Pipeline, Dashboard designer, and Pipeline. This allows us to offer customized solutions and scalable analysis to different industries. BDB's NLP-based search allows users to access the data power on desktop, tablet, and mobile. BDB is equipped with many data connectors that allow it to connect to a variety of data sources, apps, third-party API's, IoT and social media. It works in real-time. It allows you to connect to RDBMS and Big data, FTP/ SFTP Server flat files, web services, and FTP/ SFTP Server. You can manage unstructured, semi-structured, and structured data. Get started on your journey to advanced analysis today.

Open core, delivering hybrid cloud and multi-cloud integration Data Fusion is built with open source project CDAP. This open core allows users to easily port data from their projects. Cloud Data Fusion users can break down silos and get insights that were previously unavailable thanks to CDAP's integration with both on-premises as well as public cloud platforms. Integrated with Google's industry-leading Big Data Tools Data Fusion's integration to Google Cloud simplifies data security, and ensures that data is instantly available for analysis. Cloud Data Fusion integration makes it easy to develop and iterate on data lakes with Cloud Storage and Dataproc.

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.

Tarsal is infinitely scalable, so as your company grows, Tarsal will grow with you. Tarsal allows you to easily switch from SIEM data to data lake data with just one click. Keep your SIEM, and migrate analytics to a data-lake gradually. Tarsal doesn't require you to remove anything. Some analytics won't work on your SIEM. Tarsal can be used to query data in a data lake. Your SIEM is a major line item in your budget. Tarsal can be used to send some of this data to your data lake. Tarsal is a highly scalable ETL pipeline designed for security teams. With just a few mouse clicks you can easily exfiltrate terabytes with instant normalization and route the data to your destination.

Cloud Composer's managed nature with Apache Airflow compatibility allow you to focus on authoring and scheduling your workflows, rather than provisioning resources. Google Cloud products include BigQuery, Dataflow and Dataproc. They also offer integration with Cloud Storage, Cloud Storage, Pub/Sub and AI Platform. This allows users to fully orchestrate their pipeline. You can schedule, author, and monitor all aspects of your workflows using one orchestration tool. This is true regardless of whether your pipeline lives on-premises or in multiple clouds. You can make it easier to move to the cloud, or maintain a hybrid environment with workflows that cross over between the public cloud and on-premises. To create a unified environment, you can create workflows that connect data processing and services across cloud platforms.

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.

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.

RudderStack is the smart customer information pipeline. You can easily build pipelines that connect your entire customer data stack. Then, make them smarter by pulling data from your data warehouse to trigger enrichment in customer tools for identity sewing and other advanced uses cases. Start building smarter customer data pipelines today.

DataFactory has everything you need to integrate data and build efficient data pipelines. Transform raw data to information and insights faster than with other tools. No more writing pages of code just to move or transform data. Drag data operations directly from a tool pallete onto your pipeline canvas for even the most complex pipelines. Drag and drop data transformations to a pipeline canvas. Build pipelines in minutes that would have taken hours to code. Automate and operationalize by using version control and an approval mechanism. Data wrangling used to be one tool, pipeline creation was another and machine learning yet another. DataFactory brings all of these functions together. Drag-and-drop transformations make it easy to perform operations. Prepare datasets for advanced analytics. Add & operationalize ML features like segmentation and category without code.

Stripe Data Pipeline allows you to send all your Stripe data and reports directly to Amazon Redshift or Snowflake in just a few clicks. You can combine your Stripe data with business data to close your books quicker and gain more business insight. Install Stripe Data Pipeline in minutes. You will automatically receive your Stripe data, reports and data warehouse on an ongoing basis. To speed up your financial close and gain better insight, create a single source for truth. Find the best-performing payment methods and analyze fraud by location. Without the need for a third-party extract transform and load (ETL), you can send your Stripe data directly into your data warehouse. Stripe has a built-in pipeline that can handle ongoing maintenance. No matter how many data points you have, your data will always be complete and accurate. Automate data delivery at scale, minimize security risk, and avoid data outages.

Cloud Foundry and Kubernetes support microservice-based streaming and batch processing. Spring Cloud Data Flow allows you to create complex topologies that can be used for streaming and batch data pipelines. The data pipelines are made up of Spring Boot apps that were built using the Spring Cloud Stream and Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a variety of data processing use cases including ETL, import/export, event streaming and predictive analytics. Spring Cloud Data Flow server uses Spring Cloud Deployer to deploy data pipelines made from Spring Cloud Stream and Spring Cloud Task applications onto modern platforms like Cloud Foundry or Kubernetes. Pre-built stream and task/batch starter applications for different data integration and processing scenarios allow for experimentation and learning. You can create custom stream and task apps that target different middleware or services using the Spring Boot programming model.

Capture your unstructured data to meet all of your LLM requirements. Datavolo replaces point-to-point, single-use code with flexible, reusable, fast pipelines. This allows you to focus on the most important thing, which is doing amazing work. Datavolo gives you an edge in the competitive market. Get unrestricted access to your data, even the unstructured files on which LLMs depend, and boost your generative AI. You can create pipelines that will grow with you in minutes and not days. Configure instantly from any source to any location at any time. {Trust your data because lineage is built into every pipeline.|Every pipeline includes a lineage.} Pipelines and configurations that are only used once can be a thing of past. Datavolo is a powerful tool that uses Apache NiFi to harness unstructured information and unlock AI innovation. Our founders have dedicated their lives to helping organizations get the most out of their data.

The fastest way to prepare data and build data pipelines in cloud. Trifacta offers visual and intelligent guidance to speed up data preparation to help you get to your insights faster. Poor data quality can cause problems in any analytics project. Trifacta helps you to understand your data and can help you quickly and accurately clean up it. All the power without any code. Trifacta offers visual and intelligent guidance to help you get to the right insights faster. Manual, repetitive data preparation processes don't scale. Trifacta makes it easy to build, deploy, and manage self-service data networks in minutes instead of months.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows that result in a saving of ~10 hours of engineering time/week and 10x faster reporting, analytics, and decision making. The platform supports 100+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 500 data-driven companies spread across 35+ countries trust Hevo for their data integration needs.

Unravel makes data available anywhere: Azure, AWS and GCP, or in your own datacenter. Optimizing performance, troubleshooting, and cost control are all possible with Unravel. Unravel allows you to monitor, manage and improve your data pipelines on-premises and in the cloud. This will help you drive better performance in the applications that support your business. Get a single view of all your data stack. Unravel gathers performance data from every platform and system. Then, Unravel uses agentless technologies to model your data pipelines end-to-end. Analyze, correlate, and explore all of your cloud and modern data. Unravel's data models reveal dependencies, issues and opportunities. They also reveal how apps and resources have been used, and what's working. You don't need to monitor performance. Instead, you can quickly troubleshoot issues and resolve them. AI-powered recommendations can be used to automate performance improvements, lower cost, and prepare.

For AI and cloud analytics, you can ingest, prepare, or process data pipelines at large scale. Informatica's extensive data engineering portfolio includes everything you need to process big data engineering workloads for AI and analytics. This includes robust data integration, streamlining, masking, data preparation, and data quality.

Osmos allows customers to easily clean up their data files and import them directly into the operational system without having to write a single line of code. Our core product is powered by an AI-powered data transformer engine that allows users to map, validate and clean data in just a few clicks. Your account will be charged/credited according to the remaining percentage of the billing cycle at the time that the plan was modified. An eCommerce company automates the ingestion of product catalogue data from multiple vendors and distributors into their database. A manufacturing company automates the ingestion of purchase orders via email attachments into Netsuite. Automatically clean up and format the incoming data to your destination schema. Never again deal with custom scripts or spreadsheets.

Integrate sensors to IoT applications and enrich sensor data by integrating control system and environmental data. This data can be integrated with enterprise data in real-time and used to develop predictive algorithms that uncover new insights and harvest data for meaningful purposes. Analytics can be used to predict maintenance problems, analyze asset utilization, reduce defects, and optimize processes. Remote monitoring and diagnostics services can be provided by using the power of connected devices. IoT Analytics can be used to predict safety hazards and comply to regulations to reduce workplace accidents.

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

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.

GlassFlow is an event-driven, serverless data pipeline platform for Python developers. It allows users to build real time data pipelines, without the need for complex infrastructure such as Kafka or Flink. GlassFlow is a platform that allows developers to define data transformations by writing Python functions. GlassFlow manages all the infrastructure, including auto-scaling and low latency. Through its Python SDK, the platform can be integrated with a variety of data sources and destinations including Google Pub/Sub and AWS Kinesis. GlassFlow offers a low-code interface that allows users to quickly create and deploy pipelines. It also has features like serverless function executions, real-time connections to APIs, alerting and reprocessing abilities, etc. The platform is designed for Python developers to make it easier to create and manage event-driven data pipes.

Reactive Programming can be used on the back-end without the use of complex languages or cumbersome frameworks. Triggers, Actors, and Event Collections make it simple to build data pipelines. They also allow you to work with data streams using simple SQL syntax. This makes it easier for users to avoid the complexities of distributed systems development. Extensible data modeling is a key feature. It supports rich semantics, schema definition, and allows for real-world objects to be represented. Data shaping rules and validation on the fly support a variety of formats, including JSON and XML. This allows you to easily define and evolve your schema while keeping up with changing business requirements. If you can describe it, we can query it. Are you familiar with Javascript and SQL? You already know how to use the database engine. No matter what format you use, a powerful query engine allows you to instantly test logic expressions or functions. This speeds up development and simplifies deployment for unmatched data agility.

Adele is a platform that simplifies the migration of data from legacy systems to target platforms. It gives users full control over the migration process while its intelligent mapping features provide valuable insights. Adele reverse-engineers data pipelines to create data lineage maps and extract metadata, improving visibility and understanding of data flow.

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

IBM® StreamSets allows users to create and maintain smart streaming data pipelines using an intuitive graphical user interface. This facilitates seamless data integration in hybrid and multicloud environments. IBM StreamSets is used by leading global companies to support millions data pipelines, for modern analytics and intelligent applications. Reduce data staleness, and enable real-time information at scale. Handle millions of records across thousands of pipelines in seconds. Drag-and-drop processors that automatically detect and adapt to data drift will protect your data pipelines against unexpected changes and shifts. Create streaming pipelines for ingesting structured, semistructured, or unstructured data to deliver it to multiple destinations.

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.

Talend Pipeline designer is a self-service web application that transforms raw data into analytics-ready data. Create reusable pipelines for extracting, improving, and transforming data from virtually any source. Then, pass it on to your choice of destination data warehouses, where you can use it as the basis for dashboards that drive your business insights. Create and deploy data pipelines faster. With an easy visual interface, you can design and preview batch or streaming data directly in your browser. Scale your hybrid and multi-cloud technology with native support and improve productivity through real-time development. Live preview allows you to visually diagnose problems with your data. Documentation, quality assurance, and promotion of datasets will help you make better decisions faster. Transform data to improve data quality using built-in functions that can be applied across batch or stream pipelines. Data health becomes an automated discipline.

Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your 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.

Nextflow Tower is an intuitive, centralized command post that facilitates large-scale collaborative data analysis. Tower makes it easy to launch, manage, monitor, and monitor scalable Nextflow data analysis and compute environments both on-premises and on the cloud. Researchers can concentrate on the science that is important and not worry about infrastructure engineering. With predictable, auditable pipeline execution, compliance is made easier. You can also reproduce results with specific data sets or pipeline versions on-demand. Nextflow Tower was developed and supported by Seqera Labs. They are the maintainers and creators of the open-source Nextflow project. Users get high-quality support straight from the source. Tower integrates Nextflow with third-party frameworks, which is a significant advantage. It can help users take advantage of Nextflow's full range of capabilities.

Discover insights to boost sales growth with code-free, fully automated data pipelines to data lakes and cloud warehouses. Flexible, standards-based platform that unifies sales and marketing data to automate insights and smarter growth. Say goodbye to manual data downloads that are expensive and messy. You will always know exactly what you'll be charged and only pay what you actually use. Access to data-ready data is a great way to fuel your tools. We only work with official APIs as certified developers. Data pipelines from well-known sources are easy to use. These data pipelines are pre-built, pre-transformed and ready to go. Unlock data from Amazon Vendor Central and Amazon Seller Central, Instagram Stories. Teams can quickly and economically realize the value of their data with code-free data ingestion and transformation. Databricks, Amazon Redshift and other trusted data destinations like Databricks or Amazon Redshift ensure that data is always protected.

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.

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.

Lightbend technology allows developers to quickly build data-centric applications that can handle the most complex, distributed applications and streaming data streams. Lightbend is used by companies around the world to address the problems of distributed, real-time data to support their most important business initiatives. Akka Platform is a platform that makes it easy for businesses build, deploy, manage, and maintain large-scale applications that support digitally transformational initiatives. Reactive microservices are a way to accelerate time-to-value, reduce infrastructure costs, and lower cloud costs. They take full advantage the distributed nature cloud and are highly efficient, resilient to failure, and able to operate at any scale. Native support for encryption, data destruction, TLS enforcement and compliance with GDPR. Framework to quickly build, deploy and manage streaming data pipelines.

Event Hubs is a fully managed, real time data ingestion service that is simple, reliable, and scalable. Stream millions of events per minute from any source to create dynamic data pipelines that can be used to respond to business problems. Use the geo-disaster recovery or geo-replication features to continue processing data in emergencies. Integrate seamlessly with Azure services to unlock valuable insights. You can allow existing Apache Kafka clients to talk to Event Hubs with no code changes. This allows you to have a managed Kafka experience, without the need to manage your own clusters. You can experience real-time data input and microbatching in the same stream. Instead of worrying about infrastructure management, focus on gaining insights from your data. Real-time big data pipelines are built to address business challenges immediately.

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

Integrate with existing toolchain to automate self-service provisioning, refresh enterprise workloads, and integrate with existing tools. Through a rich set APIs and automation, data scientists can achieve high-performance data delivery and re-use. Any cloud data can be recovered at any time, at any scale, and beyond legacy solutions. Reduce the business impact of ransomware and cyber attacks by quickly recovering with immutable backups. Unified platform to protect, secure, keep, govern, and recover your data whether it is on-premises or cloud. Actifio's patented software platform turns data silos into data pipelines. Virtual Data Pipeline (VDP), provides full-stack data management - hybrid, on-premises, or multi-cloud -- from rich application integration, SLA based orchestration, flexible movement, data immutability, security, and SLA-based orchestration.

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.

Yandex Data Proc creates and configures Spark clusters, Hadoop clusters, and other components based on the size, node capacity and services you select. Zeppelin Notebooks and other web applications can be used to collaborate via a UI Proxy. You have full control over your cluster, with root permissions on each VM. Install your own libraries and applications on clusters running without having to restart. Yandex Data Proc automatically increases or decreases computing resources for compute subclusters according to CPU usage indicators. Data Proc enables you to create managed clusters of Hive, which can reduce failures and losses due to metadata not being available. Save time when building ETL pipelines, pipelines for developing and training models, and describing other iterative processes. Apache Airflow already includes the Data Proc operator.

Gravity's mission, to make streaming data from over 100 sources easy and only pay for what you use, is Gravity. Gravity eliminates the need for engineering teams to deliver streaming pipelines. It provides a simple interface that allows streaming to be set up in minutes using event data, databases, and APIs. All members of the data team can now create with a simple point-and-click interface so you can concentrate on building apps, services, and customer experiences. For quick diagnosis and resolution, full Execution trace and detailed error messages are available. We have created new, feature-rich methods to help you quickly get started. You can set up bulk, default schemas, and select data to access different job modes and statuses. Our intelligent engine will keep your pipelines running, so you spend less time managing infrastructure and more time analysing it. Gravity integrates into your systems for notifications, orchestration, and orchestration.

Amazon Managed Workflows (MWAA), a managed orchestration service that allows Apache Airflow to create and manage data pipelines in the cloud at scale, is called Amazon Managed Workflows. Apache Airflow is an open source tool that allows you to programmatically create, schedule, and monitor a series of processes and tasks, also known as "workflows". Managed Workflows lets you use Airflow and Python to create workflows and not have to manage the infrastructure for scalability availability and security. Managed Workflows automatically scales the workflow execution to meet your requirements. It is also integrated with AWS security services, which allows you to have fast and secure access.

All your ELT data pipelines, including custom ones, will be up and running in minutes. Your team can focus on innovation and insights. Unify all your data integration pipelines with one open-source ELT platform. Airbyte can meet all the connector needs of your data team, no matter how complex or large they may be. Airbyte is a data integration platform that scales to meet your high-volume or custom needs. From large databases to the long tail API sources. Airbyte offers a long list of connectors with high quality that can adapt to API and schema changes. It is possible to unify all native and custom ELT. Our connector development kit allows you to quickly edit and create new connectors from pre-built open-source ones. Transparent and scalable pricing. Finally, transparent and predictable pricing that scales with data needs. No need to worry about volume. No need to create custom systems for your internal scripts or database replication.