Best Data Pipeline Software for Microsoft Azure

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.

Open source solutions that require no maintenance can save you time and money. Your engineers will enjoy working with data because it is integrated, managed and highly reliable. DoubleCloud offers a range of managed open-source services, or you can leverage the full platform's power, including data storage and visualization, orchestration, ELT and real-time visualisation. We offer leading open-source solutions like ClickHouse Kafka and Airflow with deployments on Amazon Web Services and Google Cloud. Our no-code ELT allows real-time data sync between systems. It is fast, serverless and seamlessly integrated into your existing infrastructure. Our managed open-source data visualisation allows you to visualize your data in real time by creating charts and dashboards. Our platform is designed to make engineers' lives easier.

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.

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.

Microsoft Graph is the gateway for your organization to access Microsoft 365 data in order to improve productivity, identity and security. Microsoft Graph Data Connect allows developers to copy selected Microsoft 365 datasets in a secure, scalable manner into Azure data stores. It's perfect for training machine-learning and AI models to uncover rich organizational insights. Copy data from a Microsoft 365 tenant at scale and move it into Azure Data Factory directly without writing code. In just a few easy steps, you can get the data that you need delivered to your application in a repeatable manner. Microsoft Graph Data Connect's granular consent model allows you to control how your organization's information is accessed. Developers must specify what data or filters their application will access. Administrators must also explicitly approve access to Microsoft 365 data.

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.

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.

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

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.

Unify Your Data Stack: Experience the first no-code data pipeline platform and power enlightened decision making. Integrate.io is the only complete set of data solutions & connectors for easy building and managing of clean, secure data pipelines. Increase your data team's output with all of the simple, powerful tools & connectors you’ll ever need in one no-code data integration platform. Empower any size team to consistently deliver projects on-time & under budget. Integrate.io's Platform includes: -No-Code ETL & Reverse ETL: Drag & drop no-code data pipelines with 220+ out-of-the-box data transformations -Easy ELT & CDC :The Fastest Data Replication On The Market -Automated API Generation: Build Automated, Secure APIs in Minutes - Data Warehouse Monitoring: Finally Understand Your Warehouse Spend - FREE Data Observability: Custom Pipeline Alerts to Monitor Data in Real-Time

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.

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.

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.

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.

Crux is used by the most powerful people to increase external data integration, transformation and observability, without increasing their headcount. Our cloud-native data technology accelerates the preparation, observation, and delivery of any external dataset. We can guarantee you receive high-quality data at the right time, in the right format, and in the right location. Automated schema detection, delivery schedule inference and lifecycle management are all tools that can be used to quickly build pipelines from any external source of data. A private catalog of linked and matched data products will increase your organization's discoverability. To quickly combine data from multiple sources and accelerate analytics, enrich, validate, and transform any data set, you can enrich, validate, or transform it.

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.