Best Data Management Software for Dagster+

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Open-source web application, the Jupyter Notebook, allows you to create and share documents with live code, equations, and visualizations. Data cleaning and transformation, numerical modeling, statistical modeling and data visualization are just a few of the many uses.

Pandas is an open-source data analysis and manipulation tool that is fast, flexible, flexible, and easy to use. It was built on top the Python programming language. Tools for reading and writing data between memory data structures and various formats: CSV, text files, Microsoft Excel, SQL databases and the fast HDF5 format. Intelligent data alignment and integrated handling missing data: Use a powerful group engine to perform split-apply/combine operations on data sets. Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. You can even create domain-specific offsets and join time sequences without losing data.

Datadog is the cloud-age monitoring, security, and analytics platform for developers, IT operation teams, security engineers, and business users. Our SaaS platform integrates monitoring of infrastructure, application performance monitoring, and log management to provide unified and real-time monitoring of all our customers' technology stacks. Datadog is used by companies of all sizes and in many industries to enable digital transformation, cloud migration, collaboration among development, operations and security teams, accelerate time-to-market for applications, reduce the time it takes to solve problems, secure applications and infrastructure and understand user behavior to track key business metrics.

MySQL is the most widely used open-source database in the world. MySQL is the most popular open source database for web-based applications. It has been proven to be reliable, performant, and easy-to-use. This database is used by many high-profile web properties, including Facebook, Twitter and YouTube. It is also a popular choice for embedded databases, distributed by thousands ISVs and OEMs.

Your cloud data platform. Access to any data you need with unlimited scalability. All your data is available to you, with the near-infinite performance and concurrency required by your organization. You can seamlessly share and consume shared data across your organization to collaborate and solve your most difficult business problems. You can increase productivity and reduce time to value by collaborating with data professionals to quickly deliver integrated data solutions from any location in your organization. Our technology partners and system integrators can help you deploy Snowflake to your success, no matter if you are moving data into Snowflake.

Coginiti is the AI-enabled enterprise Data Workspace that empowers everyone to get fast, consistent answers to any business questions. Coginiti helps you find and search for metrics that are approved for your use case, accelerating the lifecycle of analytic development from development to certification. Coginiti integrates the functionality needed to build, approve and curate analytics for reuse across all business domains, while adhering your data governance policies and standards. Coginiti’s collaborative data workspace is trusted by teams in the insurance, healthcare, financial services and retail/consumer packaged goods industries to deliver value to customers.

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.

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.

Tobiko, a data-transformation platform, is backward compatible with databases and ships data faster, more effectively, with fewer errors. Create a development environment without having to rebuild the entire DAG. Tobiko only makes the necessary changes. Do not rebuild the entire page when you add a new column. You already built your change. Tobiko automatically promotes your prod without requiring you to redo any of your work. Avoid debugging clunky Jinja by defining your models in SQL. Tobiko is scalable for startups and enterprises. Tobiko improves developer productivity and understands the SQL that you write by detecting issues at compile time. Audits and data comparisons provide validation, making it easy to trust your datasets. Each change is analyzed, and automatically classified as breaking or not. When mistakes occur, teams can seamlessly rollback to the previous version to reduce production downtime.

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.

PostgreSQL, a powerful open-source object-relational database system, has over 30 years of experience in active development. It has earned a strong reputation for reliability and feature robustness.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Dask is free and open-source. It was developed in collaboration with other community projects such as NumPy and pandas. Dask uses existing Python data structures and APIs to make it easy for users to switch between NumPy/pandas and scikit-learn-powered versions. Dask's schedulers can scale to thousands of node clusters, and its algorithms have been tested at some of the most powerful supercomputers around the world. You don't necessarily need a large cluster to get started. Dask ships schedulers that can be used on personal computers. Many people use Dask to scale computations on their laptops, using multiple cores and their disk for extra storage. Dask exposes lower level APIs that allow you to build custom systems for your own applications. This allows open-source leaders to parallelize their own packages, and business leaders to scale custom business logic.

Azure Databricks allows you to unlock insights from all your data, build artificial intelligence (AI), solutions, and autoscale your Apache Spark™. You can also collaborate on shared projects with other people in an interactive workspace. Azure Databricks supports Python and Scala, R and Java, as well data science frameworks such as TensorFlow, PyTorch and scikit-learn. Azure Databricks offers the latest version of Apache Spark and allows seamless integration with open-source libraries. You can quickly spin up clusters and build in an Apache Spark environment that is fully managed and available worldwide. Clusters can be set up, configured, fine-tuned, and monitored to ensure performance and reliability. To reduce total cost of ownership (TCO), take advantage of autoscaling or auto-termination.

Great Expectations is a standard for data quality that is shared and openly accessible. It assists data teams in eliminating pipeline debt through data testing, documentation and profiling. We recommend that you deploy within a virtual environment. You may want to read the Supporting section if you are not familiar with pip and virtual environments, notebooks or git. Many companies have high expectations and are doing amazing things these days. Take a look at some case studies of companies we have worked with to see how they use great expectations in their data stack. Great expectations cloud is a fully managed SaaS service. We are looking for private alpha members to join our great expectations cloud, a fully managed SaaS service. Alpha members have first access to new features, and can contribute to the roadmap.

Data is used to inform every aspect of a plant or facility. It is the basis for most operational processes, business decisions, and environmental events. This data is often blamed for failures, whether it's operator error, bad sensor, safety or environmental events or poor analytics. APERIO can help solve these problems. Data integrity is a critical element of Industry 4.0. It is the foundation on which more advanced applications such as predictive models and process optimization are built. APERIO DataWise provides reliable, trusted data. Automate the quality of PI data and digital twins at scale. Validated data is required across the enterprise in order to improve asset reliability. Empowering the operator to take better decisions. Detect threats to operational data in order to ensure operational resilience. Monitor & report sustainability metrics accurately.

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.