Best Data Pipeline Software for Pantomath

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 teams can collaborate as software engineering teams by using version control, quality assurance, documentation, and modularity. Analytics errors should be treated as serious as production product bugs. Analytic workflows are often manual. We believe that workflows should be designed to be executed with one command. Data teams use dbt for codifying business logic and making it available to the entire organization. This is useful for reporting, ML modeling and operational workflows. Built-in CI/CD ensures data model changes are made in the correct order through development, staging, production, and production environments. dbt Cloud offers guaranteed uptime and custom SLAs.

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.

Fivetran is the smartest method to replicate data into your warehouse. Our zero-maintenance pipeline is the only one that allows for a quick setup. It takes months of development to create this system. Our connectors connect data from multiple databases and applications to one central location, allowing analysts to gain profound insights into their business.

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.

Qlik Compose for Data Warehouses offers a modern approach to data warehouse creation and operations by automating and optimising the process. Qlik Compose automates the design of the warehouse, generates ETL code and quickly applies updates, all while leveraging best practices. Qlik Compose for Data Warehouses reduces time, cost, and risk for BI projects whether they are on-premises, or in the cloud. Qlik Compose for Data Lakes automates data pipelines, resulting in analytics-ready data. By automating data ingestion and schema creation, as well as continual updates, organizations can realize a faster return on their existing data lakes investments.

Unified stream and batch data processing that is serverless, fast, cost-effective, and low-cost. Fully managed data processing service. Automated provisioning of and management of processing resource. Horizontal autoscaling worker resources to maximize resource use Apache Beam SDK is an open-source platform for community-driven innovation. Reliable, consistent processing that works exactly once. Streaming data analytics at lightning speed Dataflow allows for faster, simpler streaming data pipeline development and lower data latency. Dataflow's serverless approach eliminates the operational overhead associated with data engineering workloads. Dataflow allows teams to concentrate on programming and not managing server clusters. Dataflow's serverless approach eliminates operational overhead from data engineering workloads, allowing teams to concentrate on programming and not managing server clusters. Dataflow automates provisioning, management, and utilization of processing resources to minimize latency.

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.