Prefect Integrations

Tobiko is an innovative data transformation platform aimed at speeding up data delivery while improving efficiency and reducing errors, all while ensuring compatibility with current databases. It empowers developers to establish a development environment without the hassle of reconstructing the entire Directed Acyclic Graph (DAG), as it intelligently adjusts only the components that need modification. When a new column is introduced, there's no need to rebuild everything; your previous changes remain intact. Tobiko facilitates immediate promotion to production without requiring you to redo earlier work, streamlining the deployment process. Additionally, it alleviates the difficulties associated with debugging intricate Jinja templates, allowing you to define your models directly in SQL. Suitable for both startups and large corporations, Tobiko adapts to the varying demands of any organization. It understands the SQL you write and boosts developer productivity by detecting potential issues during the compilation phase. Furthermore, detailed audits and data comparisons provide essential validation, ensuring the integrity of the generated datasets. Each adjustment is meticulously assessed and classified as either breaking or non-breaking, offering insight into the effects of changes. If errors arise, teams can effortlessly revert to prior versions, significantly reducing production downtime and ensuring operational stability. This integration of various features positions Tobiko not only as a data transformation tool but also as a valuable collaborator in creating a more efficient development ecosystem, ultimately fostering innovation and agility across projects.

DataHub stands out as a dynamic open-source metadata platform designed to improve data discovery, observability, and governance across diverse data landscapes. It allows organizations to quickly locate dependable data while delivering tailored experiences for users, all while maintaining seamless operations through accurate lineage tracking at both cross-platform and column-specific levels. By presenting a comprehensive perspective of business, operational, and technical contexts, DataHub builds confidence in your data repository. The platform includes automated assessments of data quality and employs AI-driven anomaly detection to notify teams about potential issues, thereby streamlining incident management. With extensive lineage details, documentation, and ownership information, DataHub facilitates efficient problem resolution. Moreover, it enhances governance processes by classifying dynamic assets, which significantly minimizes manual workload thanks to GenAI documentation, AI-based classification, and intelligent propagation methods. DataHub's adaptable architecture supports over 70 native integrations, positioning it as a powerful solution for organizations aiming to refine their data ecosystems. Ultimately, its multifaceted capabilities make it an indispensable resource for any organization aspiring to elevate their data management practices while fostering greater collaboration among teams.

Coiled streamlines the enterprise-level use of Dask by overseeing clusters within your AWS or GCP accounts, providing a safe and effective approach to deploying Dask in production settings. With Coiled, you can establish cloud infrastructure in just a few minutes, ensuring a hassle-free deployment experience that requires minimal input from you. The platform allows you to customize the types of cluster nodes according to your specific analytical needs, enhancing the versatility of your workflows. You can utilize Dask seamlessly within Jupyter Notebooks while enjoying access to real-time dashboards that deliver insights concerning your clusters' performance. Additionally, Coiled simplifies the creation of software environments with tailored dependencies that cater to your Dask workflows. Prioritizing enterprise-level security, Coiled also offers cost-effective solutions through service level agreements, user management capabilities, and automated cluster termination when they are no longer necessary. The process of deploying your cluster on AWS or GCP is user-friendly and can be achieved in mere minutes without the need for a credit card. You can start your code from various sources, such as cloud-based services like AWS SageMaker, open-source platforms like JupyterHub, or even directly from your personal laptop, which ensures you can work from virtually anywhere. This remarkable level of accessibility and customization positions Coiled as an outstanding option for teams eager to utilize Dask efficiently and effectively. Furthermore, the combination of rapid deployment and intuitive management tools allows teams to focus on their data analysis rather than the complexities of infrastructure setup.

Great Expectations is designed as an open standard that promotes improved data quality through collaboration. This tool aids data teams in overcoming challenges in their pipelines by facilitating efficient data testing, thorough documentation, and detailed profiling. For the best experience, it is recommended to implement it within a virtual environment. Those who are not well-versed in pip, virtual environments, notebooks, or git will find the Supporting resources helpful for their learning. Many leading companies have adopted Great Expectations to enhance their operations. We invite you to explore some of our case studies that showcase how different organizations have successfully incorporated Great Expectations into their data frameworks. Moreover, Great Expectations Cloud offers a fully managed Software as a Service (SaaS) solution, and we are actively inviting new private alpha members to join this exciting initiative. These alpha members not only gain early access to new features but also have the chance to offer feedback that will influence the product's future direction. This collaborative effort ensures that the platform evolves in a way that truly meets the needs and expectations of its users while maintaining a strong focus on continuous improvement.

Effortlessly oversee a multitude of tables through advanced machine learning-based anomaly detection, complemented by a diverse range of more than 50 customized metrics. This ensures thorough management of both data and metadata while carefully tracking all asset dependencies from initial ingestion right through to business intelligence. Such a solution not only boosts productivity but also encourages collaboration between data engineers and end-users. Sifflet seamlessly integrates with your existing data environments and tools, operating efficiently across platforms such as AWS, Google Cloud Platform, and Microsoft Azure. Stay alert to the health of your data and receive immediate notifications when quality benchmarks are not met. With just a few clicks, essential coverage for all your tables can be established, and you have the flexibility to adjust the frequency of checks, their priority, and specific notification parameters all at once. Leverage machine learning algorithms to detect any data anomalies without requiring any preliminary configuration. Each rule benefits from a distinct model that evolves based on historical data and user feedback. Furthermore, you can optimize automated processes by tapping into a library of over 50 templates suitable for any asset, thereby enhancing your monitoring capabilities even more. This methodology not only streamlines data management but also equips teams to proactively address potential challenges as they arise, fostering an environment of continuous improvement. Ultimately, this comprehensive approach transforms the way teams interact with and manage their data assets.

Data is fundamental to all operations within a processing facility, acting as the cornerstone for workflows, strategic planning, and environmental oversight. However, complications often arise from this very data, leading to operator errors, faulty sensors, safety issues, or subpar analytics. APERIO is designed to effectively tackle these problems. The reliability of data is essential for Industry 4.0, supporting advanced applications such as predictive analytics, process optimization, and custom AI solutions. APERIO DataWise, known for its robust reliability, stands out as the leading source of trustworthy data. By automating the quality assurance for your PI data or digital twins in a scalable and continuous manner, organizations can guarantee validated information that enhances asset dependability. This not only enables operators to make well-informed decisions but also helps in identifying risks to operational data, which is crucial for sustaining operational resilience. Additionally, it offers accurate monitoring and reporting of sustainability metrics, thus fostering more responsible and efficient practices. In the current landscape driven by data, harnessing dependable information has transitioned from being a mere advantage to an essential requirement for achieving success. The integration of high-quality data solutions can transform the way organizations approach their operational challenges and sustainability goals.

Dask is an open-source library that is freely accessible and developed through collaboration with various community efforts like NumPy, pandas, and scikit-learn. It utilizes the established Python APIs and data structures, enabling users to move smoothly between the standard libraries and their Dask-augmented counterparts. The library's schedulers are designed to scale effectively across large clusters containing thousands of nodes, and its algorithms have been tested on some of the world’s most powerful supercomputers. Nevertheless, users do not need access to expansive clusters to get started, as Dask also includes schedulers that are optimized for personal computing setups. Many users find value in Dask for improving computation performance on their personal laptops, taking advantage of multiple CPU cores while also using disk space for extra storage. Additionally, Dask offers lower-level APIs that allow developers to build customized systems tailored to specific needs. This capability is especially advantageous for innovators in the open-source community aiming to parallelize their applications, as well as for business leaders who want to scale their innovative business models effectively. Ultimately, Dask acts as a flexible tool that effectively connects straightforward local computations with intricate distributed processing requirements, making it a valuable asset for a wide range of users.