Alternatives to Chalk

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

The only platform that can handle all aspects of data pipeline. Gathr was built from the ground up to support a cloud-first world. It is the only platform that can handle all your data integration needs - ingestion and ETL, ELT and CDC, streaming analytics and data preparation, machine-learning, advanced analytics, and more. Gathr makes it easy for anyone to build and deploy pipelines, regardless of their skill level. Ingestion pipelines can be created in minutes and not weeks. You can access data from any source and deliver it to any destination. A wizard-based approach allows you to quickly build applications. A templatized CDC app allows you to replicate data in real time. Native integration for all sources. All the capabilities you need to succeed today or tomorrow. You can choose between pay-per-use, free, or customized according to your needs.

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.

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.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

Mage transforms data into predictions. In minutes, you can build, train, then deploy predictive models. No AI experience necessary. You can increase user engagement by ranking content in your user's homefeed. Conversion can be increased by showing users the most relevant products to purchase. You can predict which users will quit using your app to increase retention. Matching users in a marketplace can increase conversion. Data is the most crucial part of building AI. Mage will help you navigate this process and offer suggestions on how to improve data. You will become an AI expert. AI and its predictions can be confusing. Mage will explain every metric in detail, showing you how your AI model thinks. With just a few lines code, you can get real-time predictions. Mage makes it easy to integrate your AI model into any application.

Prefect Cloud is a command centre for your workflows. You can instantly deploy from Prefect core to gain full control and oversight. Cloud's beautiful UI allows you to keep an eye on your infrastructure's health. You can stream real-time state updates and logs, launch new runs, and get critical information right when you need it. Prefect Cloud's managed orchestration ensures that your code and data are safe while Prefect Cloud's Hybrid Model keeps everything running smoothly. Cloud scheduler runs asynchronously to ensure that your runs start on the right time every time. Advanced scheduling options allow you to schedule parameter values changes and the execution environment for each run. You can set up custom actions and notifications when your workflows change. You can monitor the health of all agents connected through your cloud instance and receive custom notifications when an agent goes offline.

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.

Spectra is a DataOps platform that allows you to create and manage complex data pipelines. It uses a low-code user interface and domain-specific features to deliver data solutions quickly and efficiently. Maximize your ROI by reducing costs and achieving faster time-to market and time-to value. Access to more than 50 native connectors that provide data processing functions like sort, lookup, join, transform and grouping. You can process structured, semi-structured and unstructured data in batch, or real-time streaming data. Managing data processing and pipeline efficiently will help you optimize and control your infrastructure spending. Spectra's pushdown capabilities with Snowflake Data Cloud enable enterprises to take advantage of Snowflake's high performance processing power and scalable architecture.

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.

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

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

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.

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.

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.

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.

More data sources, more silos and more complexity mean more change. Data architectures are often challenged to keep up with the times. This is a problem for data-driven organizations today and can put your business at risk. A data fabric is a modern distributed architecture that uses shared data assets and optimized pipelines to address data challenges. Optimized data management and integrated capabilities that allow you to intelligently simplify, automate and accelerate your data pipelines. It's easy to deploy and adapt a distributed data architecture that suits your complex, constantly changing technology landscape. Accelerate time-to-value by unlocking your distributed cloud, hybrid, and on-premises data and delivering it where it's needed at your business's pace.

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.

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.

You can deploy production-ready change data capture pipes for high-volume, real time data replication without writing a single line code. Supercharged Change Data Capture. Arcion's distributed Change Data Capture, CDC, allows for automatic schema conversion, flexible deployment, end-to-end replication and much more. Arcion's zero-data loss architecture ensures end-to-end consistency and built-in checkpointing. You can forget about performance and scalability concerns with a distributed, highly parallel architecture that supports 10x faster data replication. Arcion Cloud is the only fully managed CDC offering. You'll enjoy autoscaling, high availability, monitoring console and more. Reduce downtime and simplify data pipelines architecture.

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.

Pitchly is more than just a data platform. We help you make the most of it. Our integrated warehouse-to worker process brings business data to life. We go beyond other enterprise data platforms. Content production is a key part of the future of work. Repeatable content can be made more accurate and faster by switching to data-driven production. Workers are then free to do higher-value work. Pitchly gives you the power to create data-driven content. You can set up brand templates and build your workflow. Then, you can enjoy on-demand publishing with the reliability of data-driven accuracy and consistency. You can manage all your assets in one content library, including tombstones, case studies and bios as well as reports and any other content assets Pitchly clients produce.

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.

Scalable Python framework designed to build real-time intelligent applications, data pipelines, and integrate AI/ML models

Many components are required to build real-time apps or services. These components include Kafka and VPC hosting, infrastructure code, container orchestration and observability. The Quix platform handles all the moving parts. Connect your data and get started building. That's it. There are no provisioning clusters nor configuring resources. You can use Quix connectors for ingesting transaction messages from your financial processing system in a virtual private clouds or on-premise data centers. For security and efficiency, all data in transit is encrypted from the beginning and compressed using Protobuf and G-Zip. Machine learning models and rule-based algorithms can detect fraudulent patterns. You can display fraud warning messages in support dashboards or as troubleshooting tickets.

Qlik Compose for Data Warehouses, formerly Attunity Compose for Data Warehouses, offers a modern approach to automating and optimizing data warehouse construction and operation. Qlik Compose automates the design of the warehouse, generation of ETL code, and applying updates quickly, all while leveraging best practices, proven design patterns, and best practices. Qlik Compose for Data Warehouses drastically reduces time, cost, and risk for BI projects, on-premises or cloud. Qlik Compose for Data Lakes, formerly Attunity Compose for Data Lakes, automates your data pipelines and creates analytics-ready data sets. Organizations can get more value from their existing data lakes investments by automating data ingestion, schema generation, and continuous updates.

It is an artificial intelligence solution that analyzes complex data and empowers decision making. This can transform industrial productivity and human life. It automates the data science process, which can reduce the workload on already overburdened teams. It breaks down data silos by intuitively creating data pipelines that feed live data into deployed model and then dynamically creating model execution pipelines to make real-time predictions based on incoming data. It monitors the health and maintenance of deployed models using the confidence of predicted results.

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.

PredictSense is an AI-powered machine learning platform that uses AutoML to power its end-to-end Machine Learning platform. Accelerating machine intelligence will fuel the technological revolution of tomorrow. AI is key to unlocking the value of enterprise data investments. PredictSense allows businesses to quickly create AI-driven advanced analytical solutions that can help them monetize their technology investments and critical data infrastructure. Data science and business teams can quickly develop and deploy robust technology solutions at scale. Integrate AI into your existing product ecosystem and quickly track GTM for new AI solution. AutoML's complex ML models allow you to save significant time, money and effort.

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.

Apache PredictionIO®, an open-source machine-learning server, is built on top a state of the art open-source stack that allows data scientists and developers to create predictive engines for any type of machine learning task. It allows you to quickly create and deploy an engine as web service on production using customizable templates. Once deployed as a web-service, it can respond to dynamic queries immediately, evaluate and tune multiple engine variations systematically, unify data from multiple platforms either in batch or real-time for comprehensive predictive analysis. Machine learning modeling can be speeded up with pre-built evaluation methods and systematic processes. These measures also support machine learning and data processing libraries like Spark MLLib or OpenNLP. You can create your own machine learning models and integrate them seamlessly into your engine. Data infrastructure management simplified. Apache PredictionIO®, a complete machine learning stack, can be installed together with Apache Spark, MLlib and HBase.

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.

A fully-featured machine learning platform empowers enterprises to conduct real data science at scale and speed. You can spend less time managing infrastructure and tools so that you can concentrate on building machine learning applications to propel your business forward. Anaconda Enterprise removes the hassle from ML operations and puts open-source innovation at the fingertips. It provides the foundation for serious machine learning and data science production without locking you into any specific models, templates, workflows, or models. AE allows data scientists and software developers to work together to create, test, debug and deploy models using their preferred languages. AE gives developers and data scientists access to both notebooks as well as IDEs, allowing them to work more efficiently together. They can also choose between preconfigured projects and example projects. AE projects can be easily moved from one environment to the next by being automatically packaged.

Unified platform that is high-performance and can be layered on any existing infrastructure to facilitate seamless onboarding. Metrolink's intuitive design allows any organization to manage its data integration. It provides advanced manipulations that aim to maximize diverse and complex data and refocus human resource to eliminate overhead. Complex, multi-source, streaming data that is constantly changing in use cases. The focus is on core business and not data utilities. Metrolink is a Unified platform which allows organizations to design and manage their data pipes according to their business needs. This is achieved by providing an intuitive UI and advanced manipulations of complex data. It also allows for data privacy and data value enhancement.

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.

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.

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.

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.

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

Data-driven organizations are constantly striving to become more data-driven. They build dashboards, analytics and data pipelines throughout the modern data stack. Unfortunately, data reliability issues are a major problem for most organizations, leading to poor decisions and a lack of trust in the data as an organisation, which directly impacts their bottom line. Resolving complex issues is a time-consuming and manual process that involves multiple teams, all of whom rely on tribal knowledge. They manually reverse-engineer complex data pipelines across various platforms to identify the root-cause and to understand the impact. Pantomath, a data pipeline traceability and observability platform, automates data operations. It continuously monitors datasets across the enterprise data ecosystem, providing context to complex data pipes by creating automated cross platform technical pipeline lineage.

Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.

Meltano offers the most flexibility in deployment options. You control your data stack from beginning to end. Since years, a growing number of connectors has been in production. You can run workflows in isolated environments and execute end-to-end testing. You can also version control everything. Open source gives you the power and flexibility to create your ideal data stack. You can easily define your entire project in code and work confidently with your team. The Meltano CLI allows you to quickly create your project and make it easy to replicate data. Meltano was designed to be the most efficient way to run dbt and manage your transformations. Your entire data stack can be defined in your project. This makes it easy to deploy it to production.

With your own data shop, create new revenue streams from the data you already have. Narrative focuses on the fundamental principles that make buying or selling data simpler, safer, and more strategic. You must ensure that the data you have access to meets your standards. It is important to know who and how the data was collected. Access new supply and demand easily for a more agile, accessible data strategy. You can control your entire data strategy with full end-to-end access to all inputs and outputs. Our platform automates the most labor-intensive and time-consuming aspects of data acquisition so that you can access new data sources in days instead of months. You'll only ever have to pay for what you need with filters, budget controls and automatic deduplication.

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.

StreamNative redefines the streaming infrastructure by integrating Kafka MQ and other protocols into a unified platform that provides unparalleled flexibility and efficiency to modern data processing requirements. StreamNative is a unified platform that adapts to diverse streaming and messaging requirements in a microservices environment. StreamNative's comprehensive and intelligent approach to streaming and messaging empowers organizations to navigate with efficiency and agility the complexity and scalability in the modern data ecosystem. Apache Pulsar’s unique architecture decouples message storage from the message serving layer, resulting in a cloud-native data streaming platform. Scalable and elastic, allowing it to adapt to changing business needs and event traffic. Scale up to millions of topics using architecture that decouples computing from storage.

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.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

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.

Data Taps lets you build your data pipelines using Lego blocks. Add new metrics, zoom out, and investigate using real-time streaming SQL. Globally share and consume data with others. Update and refine without hassle. Use multiple models/schemas during schema evolution. Built for AWS Lambda, S3, and Lambda.