Warp 10 Integrations

Amazon Simple Storage Service (Amazon S3), an object storage service, offers industry-leading scalability and data availability, security, performance, and scalability. Customers of all sizes and industries can use Amazon S3 to store and protect any amount data for a variety of purposes, including data lakes, websites and mobile applications, backup, restore, archive, enterprise apps, big data analytics, and IoT devices. Amazon S3 offers easy-to-use management tools that allow you to organize your data and set up access controls that are tailored to your business, organizational, or compliance needs. Amazon S3 is built for 99.999999999% (11 9,'s) of durability and stores data for millions applications for companies around the globe. You can scale your storage resources to meet changing demands without having to invest upfront or go through resource procurement cycles. Amazon S3 is designed to last 99.999999999% (11 9,'s) of data endurance.

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.

Apache Kafka®, is an open-source distributed streaming platform.

Search, observability, security, and enterprise search for the cloud. Whether you use Amazon Web Services, Google Cloud or Microsoft Azure, you can quickly and easily find the information you need, gain insights, protect your investment in technology, and do so with ease. We take care of the maintenance so that you can concentrate on the things that matter to you. It's easy to configure and deploy. You can scale easily, use custom plugins and optimize your architecture for log and time series data. You can get the full Elastic experience, including machine learning, Canvas and APM, index lifecycle management as well as Elastic App Search and Elastic Workplace Search. Logging and metrics are only the beginning. To address security, observability and other critical use cases, you can bring together your diverse data.

Apache Avro™, a data serialization software, is available. Avro offers rich data structures, a compact and fast binary data format, a container, to store persistent information, remote procedure calls (RPC), and more. It also allows for easy integration with dynamic languages. It is not necessary to generate code to read and write data files or to implement RPC protocols. Only statically typed languages can use code generation. Schemas are essential for Avro. The schema used to write Avro data is always available when Avro data are read. This allows each datum to be written quickly and without any per-value overheads. This allows for dynamic, scripting languages to be used. Data, along with its schema, are fully self-describing. If Avro data is saved in a file, the schema is also stored with it. This allows programs to later process files. This can be resolved if the program reading the data expects something different.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

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.

Web-based notebook that allows data-driven, interactive data analysis and collaborative documents with SQL and Scala. The IPython interpreter offers a similar user experience to Jupyter Notebook. This release features Note level dynamic form, note comparison comparator, and the ability to run paragraph sequentially instead of simultaneous execution in previous releases. Interpreter lifecycle manager automatically terminates interpreter process upon idle timeout. So resources are released when not in use.

A reliable, easy-to-use, and powerful system to process and distribute data. Apache NiFi supports powerful, scalable directed graphs for data routing, transformation, system mediation logic, and is scalable. Apache NiFi's high-level capabilities and goals include a web-based user interface that provides seamless design, control, feedback and monitoring. Highly configurable, loss-tolerant, low latency and high throughput. Dynamic prioritization is also possible. Flow can be modified at runtime by back pressure, data provenance, and track dataflow from start to finish. This is a flexible system that is extensible. You can build your own processors. This allows for rapid development and efficient testing. Secure, SSL, SSH and HTTPS encryption, as well as encrypted content. Multi-tenant authorization, internal authorization/policy administration. NiFi includes a variety of web applications, including web UI, web API, documentation and custom UI's. You will need to map to the root path.

Parquet was created to provide the Hadoop ecosystem with the benefits of columnar, compressed data representation. Parquet was built with complex nested data structures and uses the Dremel paper's record shredding/assemblage algorithm. This approach is better than flattening nested namespaces. Parquet is designed to support efficient compression and encoding strategies. Multiple projects have shown the positive impact of the right compression and encoding scheme on data performance. Parquet allows for compression schemes to be specified per-column. It is future-proofed to allow for more encodings to be added as they are developed and implemented. Parquet was designed to be used by everyone. We don't want to play favorites in the Hadoop ecosystem.

Apache Flink is a distributed processing engine and framework for stateful computations using unbounded and bounded data streams. Flink can be used in all cluster environments and perform computations at any scale and in-memory speed. A stream of events can be used to produce any type of data. All data, including credit card transactions, machine logs, sensor measurements, and user interactions on a website, mobile app, are generated as streams. Apache Flink excels in processing both unbounded and bound data sets. Flink's runtime can run any type of application on unbounded stream streams thanks to its precise control of state and time. Bounded streams are internal processed by algorithms and data structure that are specifically designed to process fixed-sized data sets. This results in excellent performance. Flink can be used with all of the resource managers previously mentioned.