Best Data Management Software for Apache Zeppelin

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Elastic is a search company. Elasticsearch, Kibana Beats, Logstash, and Elasticsearch are the founders of the ElasticStack. These SaaS offerings allow data to be used in real-time and at scale for analytics, security, search, logging, security, and search. Elastic has over 100,000 members in 45 countries. Elastic's products have been downloaded more than 400 million times since their initial release. Today, thousands of organizations including Cisco, eBay and Dell, Goldman Sachs and Groupon, HP and Microsoft, as well as Netflix, Uber, Verizon and Yelp use Elastic Stack and Elastic Cloud to power mission critical systems that generate new revenue opportunities and huge cost savings. Elastic is headquartered in Amsterdam, The Netherlands and Mountain View, California. It has more than 1,000 employees in over 35 countries.

The Apache Cassandra database provides high availability and scalability without compromising performance. It is the ideal platform for mission-critical data because it offers linear scalability and demonstrated fault-tolerance with commodity hardware and cloud infrastructure. Cassandra's ability to replicate across multiple datacenters is first-in-class. This provides lower latency for your users, and the peace-of-mind that you can withstand regional outages.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

Warp 10 is a modular open source platform that collects, stores, and allows you to analyze time series and sensor data. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 offers both a time series database and a powerful analysis environment, which can be used together or independently. It will allow you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The Platform is GDPR compliant and secure by design using cryptographic tokens to manage authentication and authorization. The Analytics Engine can be implemented within a large number of existing tools and ecosystems such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. From small devices to distributed clusters, Warp 10 fits your needs at any scale, and can be used in many verticals: industry, transportation, health, monitoring, finance, energy, etc.

Yandex Data Proc creates and configures Spark clusters, Hadoop clusters, and other components based on the size, node capacity and services you select. Zeppelin Notebooks and other web applications can be used to collaborate via a UI Proxy. You have full control over your cluster, with root permissions on each VM. Install your own libraries and applications on clusters running without having to restart. Yandex Data Proc automatically increases or decreases computing resources for compute subclusters according to CPU usage indicators. Data Proc enables you to create managed clusters of Hive, which can reduce failures and losses due to metadata not being available. Save time when building ETL pipelines, pipelines for developing and training models, and describing other iterative processes. Apache Airflow already includes the Data Proc operator.

Apache HBase™, is used when you need random, real-time read/write access for your Big Data. This project aims to host very large tables, billions of rows and X million columns, on top of clusters of commodity hardware.

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 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.

High-speed, data-intensive apps that meet all performance requirements can be built. Apache Geode's unique technology combines advanced techniques for data replication and partitioning with distributed processing. Apache Geode offers a database-like consistency model, reliable transactions processing, and a shared nothing architecture to maintain very low latency with high concurrency. Data can be easily partitioned (sharded), or replicated among nodes, allowing for performance scaling as required. Reliable in-memory copies are used to ensure durability. Disk-based persistence is also used to ensure longevity. Super fast write-ahead log (WAL) persistence that uses a shared-nothing architecture optimized for parallel recovery of nodes and entire clusters.

SQL is a domain-specific programming language that allows you to access, manage, and manipulate relational databases and relational management systems.

All work can be synced, searched and managed across your data science team. Zepl's powerful search allows you to discover and reuse models, code, and other data. Zepl's enterprise collaboration platform allows you to query data from Snowflake or Athena and then build your models in Python. For enhanced interactions with your data, use dynamic forms and pivoting. Zepl creates new containers every time you open your notebook. This ensures that you have the same image each time your models are run. You can invite your team members to join you in a shared space, and they will be able to work together in real-time. Or they can simply leave comments on a notebook. You can share your work with fine-grained access controls. You can allow others to read, edit, run, and share your work. This will facilitate collaboration and distribution. All notebooks can be saved and versioned automatically. An easy-to-use interface allows you to name, manage, roll back, and roll back all versions. You can also export seamlessly into Github.

QueryPie allows you to centrally manage multiple data sources and security policies from one place. Your company can be on the fast track for success without having to change the data environment. Data governance is essential in today's data-driven world. You must ensure that you adhere to data governance standards and allow many users accessing growing amounts of important information. You can establish data access policies by adding key attributes like IP address and access time. To secure data analysis and editing, privilege types can be created using SQL commands such as DML, DCL and DDL. You can view logs based upon permissions to see details of SQL events and uncover user behavior and security concerns. All history can be exported to a file for reporting purposes.

The smart semantic layer unifies metrics and speeds up the delivery of data products by 90% with shorter SQL queries. Model data using business terms for a common meaning and to align business metrics. Define semantic relationships to replace JOINs, making queries much easier. Hierarchies and classifications can help you better understand data. Automatically map data into the semantic model. Join multiple data sources using a powerful SQL engine distributed to query data at a large scale. Consume data in the form of a semantically connected graph. Materialized views and an intelligent cache engine can boost performance and reduce compute costs. Advanced query optimizations are available. Connect to any file format, cloud, datalake, data warehouse, or database. Timbr allows you to work seamlessly with your data sources. Timbr optimizes a query and pushes it to the backend when a query is executed.

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.

You can use Ignite as a traditional SQL Database by leveraging JDBC drivers or ODBC drivers. Or, you can use the native SQL APIs for Java, C# and C++, Python, or other programming languages. You can easily join, group, aggregate, or order your distributed on-disk and in-memory data. You can accelerate your existing applications up to 100x by using Ignite as an in memory cache or in-memory grid that is deployed over one of several external databases. You can query, transact, and calculate on this cache. Ignite is a database that scales beyond your memory capacity to support modern transactional and analytical workloads. Ignite allocates memory to your hot data and writes to disk when applications query cold records. Execute custom code up to kilobytes in size over petabytes. Your Ignite database can be transformed into a distributed supercomputer that can perform low-latency calculations, complex analysis, and machine learning.

OctoData can be deployed in Cloud hosting at a lower price and includes personalized support, from the initial definition of your needs to the actual use of the solution. OctoData is built on open-source technologies that are innovative and can adapt to new possibilities. Its Supervisor provides a management interface that allows users to quickly capture, store, and exploit increasing amounts and varieties of data. OctoData allows you to quickly prototype and industrialize massive data recovery solutions, even in real-time, in a single environment. You can get precise reports, explore new options, increase productivity, and increase profitability by leveraging your data.