Alternatives to Hadoop

Our goal was to create user-friendly ETL software that is easy to use and can be used immediately after installation. Our ETL software is easy to use by non-technical staff. There is no need for IT department assistance. Our ETL software allows any business, large or small, to automate routine processes and focus on what is most important: growing their business. By combining simple Package actions together, Advanced ETL Processor Enterprise assists businesses and Fortune 100 companies to build complex data-warehouses that automate complex business processes. The Advanced ETL Processor was developed by people who have years of experience in implementing data warehouses. Perform advanced data validation, transformation.

Amazon Redshift is preferred by more customers than any other cloud data storage. Redshift powers analytic workloads for Fortune 500 companies and startups, as well as everything in between. Redshift has helped Lyft grow from a startup to multi-billion-dollar enterprises. It's easier than any other data warehouse to gain new insights from all of your data. Redshift allows you to query petabytes (or more) of structured and semi-structured information across your operational database, data warehouse, and data lake using standard SQL. Redshift allows you to save your queries to your S3 database using open formats such as Apache Parquet. This allows you to further analyze other analytics services like Amazon EMR and Amazon Athena. Redshift is the fastest cloud data warehouse in the world and it gets faster each year. The new RA3 instances can be used for performance-intensive workloads to achieve up to 3x the performance compared to any cloud data warehouse.

Scality offers file and object storage to support enterprise data management deployments of any size. Scality adapts to your environment. Traditional on-prem storage? No problem. How do you store modern cloud-native apps? We've got you covered. Scality has a track record of protecting eleven 9s data and ensuring its durability for long-term protection.

Organizations and enterprises are creating, analyzing, and keeping more data than ever. Complexity, increased costs and difficult to manage systems are all the consequences of creating islands of data in organizations and the cloud. Leaders in their industry are those who can deliver faster insights while simultaneously managing rapid infrastructure growth. An organization's underlying information architecture must be able to support hybrid cloud, big-data and artificial intelligence (AI), as well as traditional applications, while also ensuring data efficiency, security, reliability and high performance. IBM Spectrum Scale™, a parallel, high-performance solution that allows for global file and object access to manage data at scale and has the unique ability to perform analysis and archive in place, meets these challenges.

Businesses struggle to find answers as data volumes increase faster than ever. Teradata Vantage™, solves this problem. Vantage uses 100 per cent of the data available to uncover real-time intelligence at scale. This is the new era in Pervasive Data Intelligence. All data across the organization is available in one place. You can access it whenever you need it using preferred languages and tools. Start small and scale up compute or storage to areas that have an impact on modern architecture. Vantage unifies analytics and data lakes in the cloud to enable business intelligence. Data is growing. Business intelligence is becoming more important. Four key issues that can lead to frustration when using existing data analysis platforms include: Lack of the right tools and supportive environment required to achieve quality results. Organizations don't allow or give proper access to the tools they need. It is difficult to prepare data.

SAP HANA is an in-memory database with high performance that accelerates data-driven decision-making and actions. It supports all workloads and provides the most advanced analytics on multi-model data on premise and in cloud.

MinIO's high performance object storage suite is software-defined and allows customers to create cloud-native data infrastructures for machine learning, analytics, and application data workloads. MinIO object storage is fundamentally unique. It is 100% open-source and designed for performance and the S3 API. MinIO is ideal to host large, private cloud environments that have strict security requirements. It also delivers mission-critical availability across a wide range of workloads. MinIO is the fastest object storage server in the world. With READ/WRITE speeds up to 183 GB/s on standard hardware and 171GB/s on SSDs, object storage can be used as the primary storage tier for a variety of workloads, including Spark, Presto TensorFlow, Spark, TensorFlow, H2O.ai, and as a replacement for Hadoop HDFS. MinIO uses the hard-earned knowledge of web scalers to bring object storage a simple scaling model. MinIO scales with one cluster that can be federated to other MinIO clusters.

PySpark is a Python interface for Apache Spark. It allows you to create Spark applications using Python APIs. Additionally, it provides the PySpark shell that allows you to interactively analyze your data in a distributed environment. PySpark supports Spark's most popular features, including Spark SQL, DataFrame and Streaming. Spark SQL is a Spark module that allows structured data processing. It can be used as a distributed SQL query engine and a programming abstraction called DataFrame. The streaming feature in Apache Spark, which runs on top of Spark allows for powerful interactive and analytic applications across streaming and historical data. It also inherits Spark's ease-of-use and fault tolerance characteristics.

The Unified Analytics Warehouse. The Unified Analytics Warehouse is the best place to find high-performing analytics and machine learning at large scale. Tech research analysts are seeing new leaders as they strive to deliver game-changing big data analytics. Vertica empowers data-driven companies so they can make the most of their analytics initiatives. It offers advanced time-series, geospatial, and machine learning capabilities, as well as data lake integration, user-definable extensions, cloud-optimized architecture and more. Vertica's Under the Hood webcast series allows you to dive into the features of Vertica - delivered by Vertica engineers, technical experts, and others - and discover what makes it the most scalable and scalable advanced analytical data database on the market. Vertica supports the most data-driven disruptors around the globe in their pursuit for industry and business transformation.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

Apache Ignite is an enterprise-grade platform that offers in-memory speed, massive scalability and real-time access across datastores. You can upgrade from Ignite or GridGain without any code changes and deploy your clusters securely on a global scale with zero downtime. Rolling upgrades can be performed on your production clusters without affecting application availability. To load balance workloads and prevent outages in regional areas, replicate across globally distributed data centres. You can protect your data in motion and at rest, and comply with security and privacy standards. Integrate with your organization's authorization and authentication system. Allow full data and user activity auditing. Automated schedules can be created for incremental and full backups. With snapshots and point in time recovery, restore your cluster to its last stable state.

Your apps can be freed. Reduce complexity in your operations. Software proficiency is essential to win in today's business world. How can you increase the feature velocity of the workloads that power your company? Or run and manage modernized workloads in any cloud. VMware Tanzu, when used with VMware Pivotal Labs, enables you to transform your team and your applications while simplifying operations across multicloud infrastructure: on-premises and public cloud.

Amazon EMR is the market-leading cloud big data platform. It processes large amounts of data with open source tools like Apache Spark, Apache Hive and Apache HBase. EMR allows you to run petabyte-scale analysis at a fraction of the cost of traditional on premises solutions. It is also 3x faster than standard Apache Spark. You can spin up and down clusters for short-running jobs and only pay per second for the instances. You can also create highly available clusters that scale automatically to meet the demand for long-running workloads. You can also run EMR clusters from AWS Outposts if you have on-premises open source tools like Apache Spark or Apache Hive.

Secure and manage the data lifecycle, from Edge to AI in any cloud or data centre. Operates on all major public clouds as well as the private cloud with a public experience everywhere. Integrates data management and analytics experiences across the entire data lifecycle. All environments are covered by security, compliance, migration, metadata management. Open source, extensible, and open to multiple data stores. Self-service analytics that is faster, safer, and easier to use. Self-service access to multi-function, integrated analytics on centrally managed business data. This allows for consistent experiences anywhere, whether it is in the cloud or hybrid. You can enjoy consistent data security, governance and lineage as well as deploying the cloud analytics services that business users need. This eliminates the need for shadow IT solutions.

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.

This is the easiest way to perform batch and streaming data processing. For mission-critical production workloads, write once and run anywhere data processing. Beam can read your data from any supported source, whether it's on-prem and in the cloud. Beam executes your business logic in both batch and streaming scenarios. Beam converts the results of your data processing logic into the most popular data sinks. A single programming model that can be used for both streaming and batch use cases. This is a simplified version of the code for all members of your data and applications teams. Apache Beam is extensible. TensorFlow Extended, Apache Hop and other projects built on Apache Beam are examples of Apache Beam's extensibility. Execute pipelines in multiple execution environments (runners), allowing flexibility and avoiding lock-in. Open, community-based development and support are available to help you develop your application and meet your specific needs.

Delta Lake is an open-source storage platform that allows ACID transactions to Apache Spark™, and other big data workloads. Data lakes often have multiple data pipelines that read and write data simultaneously. This makes it difficult for data engineers to ensure data integrity due to the absence of transactions. Your data lakes will benefit from ACID transactions with Delta Lake. It offers serializability, which is the highest level of isolation. Learn more at Diving into Delta Lake - Unpacking the Transaction log. Even metadata can be considered "big data" in big data. Delta Lake treats metadata the same as data and uses Spark's distributed processing power for all its metadata. Delta Lake is able to handle large tables with billions upon billions of files and partitions at a petabyte scale. Delta Lake allows developers to access snapshots of data, allowing them to revert to earlier versions for audits, rollbacks, or to reproduce experiments.

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.

All your data, analytics, and AI in one unified platform. Databricks is powered by Delta Lake. It combines the best data warehouses with data lakes to create a lakehouse architecture that allows you to collaborate on all your data, analytics, and AI workloads. We are the original developers of Apache Spark™, Delta Lake, and MLflow. We believe open source software is the key to the future of data and AI. Your business can be built on an open, cloud-agnostic platform. Databricks supports customers all over the world on AWS, Microsoft Azure, or Alibaba cloud. Our platform integrates tightly with the cloud providers' security, compute storage, analytics and AI services to help you unify your data and AI workloads.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Varada's adaptive and dynamic big data indexing solution allows you to balance cost and performance with zero data-ops. Varada's big data indexing technology is a smart acceleration layer for your data lake. It remains the single source and truth and runs in the customer's cloud environment (VPC). Varada allows data teams to democratize data. It allows them to operationalize the entire data lake and ensures interactive performance without the need for data to be moved, modelled, or manually optimized. Our ability to dynamically and automatically index relevant data at the source structure and granularity is our secret sauce. Varada allows any query to meet constantly changing performance and concurrency requirements of users and analytics API calls. It also keeps costs predictable and under control. The platform automatically determines which queries to speed up and which data to index. Varada adjusts the cluster elastically to meet demand and optimize performance and cost.

Kylo is an enterprise-ready open-source data lake management platform platform for self-service data ingestion and data preparation. It integrates metadata management, governance, security, and best practices based on Think Big's 150+ big-data implementation projects. Self-service data ingest that includes data validation, data cleansing, and automatic profiling. Visual sql and an interactive transformation through a simple user interface allow you to manage data. Search and explore data and metadata. View lineage and profile statistics. Monitor the health of feeds, services, and data lakes. Track SLAs and troubleshoot performance. To enable user self-service, create batch or streaming pipeline templates in Apache NiFi. While organizations can spend a lot of engineering effort to move data into Hadoop, they often struggle with data governance and data quality. Kylo simplifies data ingest and shifts it to data owners via a simple, guided UI.

Talend Data Fabric's cloud services are able to efficiently solve all your integration and integrity problems -- on-premises or in cloud, from any source, at any endpoint. Trusted data delivered at the right time for every user. With an intuitive interface and minimal coding, you can easily and quickly integrate data, files, applications, events, and APIs from any source to any location. Integrate quality into data management to ensure compliance with all regulations. This is possible through a collaborative, pervasive, and cohesive approach towards data governance. High quality, reliable data is essential to make informed decisions. It must be derived from real-time and batch processing, and enhanced with market-leading data enrichment and cleaning tools. Make your data more valuable by making it accessible internally and externally. Building APIs is easy with the extensive self-service capabilities. This will improve customer engagement.

Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

Lentiq is a data lake that allows small teams to do big tasks. You can quickly run machine learning, data science, and data analysis at scale in any cloud. Lentiq allows your teams to ingest data instantly and then clean, process, and share it. Lentiq allows you to create, train, and share models within your organization. Lentiq allows data teams to collaborate and invent with no restrictions. Data lakes are storage and process environments that provide ML, ETL and schema-on-read querying capabilities. Are you working on data science magic? A data lake is a must. The big, centralized data lake of the Post-Hadoop era is gone. Lentiq uses data pools, which are interconnected, multi-cloud mini-data lakes. They all work together to provide a stable, secure, and fast data science environment.

Dataleyk is a secure, fully-managed cloud platform for SMBs. Our mission is to make Big Data analytics accessible and easy for everyone. Dataleyk is the missing piece to achieving your data-driven goals. Our platform makes it easy to create a stable, flexible, and reliable cloud data lake without any technical knowledge. All of your company data can be brought together, explored with SQL, and visualized with your favorite BI tool. Dataleyk will modernize your data warehouse. Our cloud-based data platform is capable of handling both structured and unstructured data. Data is an asset. Dataleyk, a cloud-based data platform, encrypts all data and offers data warehousing on-demand. Zero maintenance may not be an easy goal. It can be a catalyst for significant delivery improvements, and transformative results.

A single storage platform can eliminate data silos. Tiered storage and policy management can help you reduce costs. Azure Active Directory (Azure AD), and role-based access control(RBAC) can authenticate data. You can also help protect your data with advanced threat protection and encryption at rest. Flexible mechanisms provide protection for data access, encryption, network-level control, and more. Highly secure. A single storage platform that supports all the most popular analytics frameworks. Cost optimization through independent scaling of storage, compute, lifecycle management and object-level Tiering. With the Azure global infrastructure, you can meet any capacity requirement and manage data with ease. Large-scale analytics queries run at high performance.

BryteFlow creates the most efficient and automated environments for analytics. It transforms Amazon S3 into a powerful analytics platform by intelligently leveraging AWS ecosystem to deliver data at lightning speed. It works in conjunction with AWS Lake Formation and automates Modern Data Architecture, ensuring performance and productivity.

Log analytics shouldn't break the bank. The cost of operation is high because most logging solutions use either Elasticsearch database or Lucene index. ChaosSearch is a new approach. ChaosSearch has redesigned indexing which allows us to pass significant cost savings on to our customers. This price comparison calculator will allow you to see the difference. ChaosSearch is a fully managed SaaS platform which allows you to concentrate on search and analytics in AWS S3 and not spend time tuning databases. Let us manage your existing AWS S3 infrastructure. Watch this video to see how ChaosSearch addresses today's data and analytic challenges.

Apache Storm is an open-source distributed realtime computing system that is free and open-source. Apache Storm makes it simple to process unbounded streams and data reliably, much like Hadoop did for batch processing. Apache Storm is easy to use with any programming language and is a lot fun! Apache Storm can be used for many purposes: realtime analytics and online machine learning. It can also be used with any programming language. Apache Storm is fast. A benchmark measured it at more than a million tuples per second per node. It is highly scalable, fault-tolerant and guarantees that your data will be processed. It is also easy to set up. Apache Storm can be integrated with the queueing and databases technologies you already use. Apache Storm topology processes streams of data in arbitrarily complex ways. It also partitions the streams between each stage of the computation as needed. Learn more in the tutorial.

EMR allows you to scale managed Hadoop clusters manually, or automatically, according to your monitoring metrics or business curves. EMR's storage computation separation allows you to terminate clusters to maximize resource efficiency. EMR supports hot failover on CBS-based nodes. It has a primary/secondary disaster recovery mechanism that allows the secondary node to start within seconds of the primary node failing, ensuring high availability of big data services. Remote disaster recovery is possible because of the metadata in Hive's components. High data persistence is possible with computation-storage separation for COS data storage. EMR comes with a comprehensive monitoring system that allows you to quickly locate and identify cluster exceptions in order to ensure stable cluster operations. VPCs are a convenient network isolation method that allows you to plan your network policies for managed Hadoop clusters.

A distributed data integration framework which simplifies common Big Data integration tasks such as data ingestion and replication, organization, and lifecycle management. It can be used for both streaming and batch data ecosystems. It can be run as a standalone program on a single computer. Also supports embedded mode. It can be used as a mapreduce application on multiple Hadoop versions. Azkaban is also available for the launch of mapreduce jobs. It can run as a standalone cluster, with primary and worker nodes. This mode supports high availability, and can also run on bare metals. This mode can be used as an elastic cluster in the public cloud. This mode supports high availability. Gobblin, as it exists today, is a framework that can build various data integration applications such as replication, ingest, and so on. Each of these applications are typically set up as a job and executed by Azkaban, a scheduler.

EMR is an enterprise-ready big-data platform that offers cluster, job, data management and other services. It is based on open-source ecosystems such as Hadoop Spark, Kafka and Flink. Alibaba Cloud Elastic MapReduce is a big-data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS and is based open-source Apache Spark and Apache Hadoop. EMR allows you use the Hadoop/Spark ecosystem components such as Apache Hive and Apache Kafka, Flink and Druid to analyze and process data. EMR can be used to process data stored on different Alibaba Cloud data storage services, such as Log Service (SLS), Object Storage Service(OSS), and Relational Data Service (RDS). It is easy to create clusters quickly without having to install hardware or software. Its Web interface allows you to perform all maintenance operations.

Arcadia Data is the first native Hadoop and cloud (big) visual analytics and BI platform that provides the scale, performance and agility business users require for real-time and historical insight. Arcadia Enterprise, its flagship product, was created from the beginning for big data platforms like Apache Hadoop, Apache Spark and Apache Kafka. It can be used on-premises or in the cloud. Arcadia Enterprise uses artificial intelligence (AI), machine learning (ML) to streamline the self-service analytics process. It offers search-based BI, visualization recommendations, and a streamlined self-service analytics process. It provides real-time, high definition insights in use cases such as data lakes, cybersecurity, and customer intelligence. Some of the most recognizable brands in the world use Arcadia Enterprise, including Procter & Gamble and Nokia, Procter & Gamble and Citibank, Nokia, Royal Bank of Canada. Kaiser Permanente, HPE and Neustar.

iomete platform combines a powerful lakehouse with an advanced data catalog, SQL editor and BI, providing you with everything you need to become data-driven.

Run popular open-source frameworks--including Apache Hadoop, Spark, Hive, Kafka, and more--using Azure HDInsight, a customizable, enterprise-grade service for open-source analytics. You can process huge amounts of data quickly and enjoy all the benefits of the large open-source project community with the global scale Azure. You can easily migrate your big data workloads to the cloud. Open-source projects, clusters and other software are easy to set up and manage quickly. Big data clusters can reduce costs by using autoscaling and pricing levels that allow you only to use what you use. Data protection is assured by enterprise-grade security and industry-leading compliance, with over 30 certifications. Optimized components for open source technologies like Hadoop and Spark keep your up-to-date.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

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.

In-Memory Computing Platform. Digital world is different. Microseconds are important. The world's most important organizations rely on us for powering their most sensitive applications at scale. If they meet the current requirement for immediate access, new data-enabled apps can transform your business. Hazelcast solutions can be used to complement any database and deliver results that are much faster than traditional systems of record. Hazelcast's distributed architecture ensures redundancy and continuous cluster up-time, as well as always available data to support the most demanding applications. The capacity grows with demand without compromising performance and availability. The cloud delivers the fastest in-memory data grid and third-generation high speed event processing.

When you have the expertise of an enterprise partner combined with a scalable, easy-to-use data management suite, you can take back control of your data, access it from anywhere, ensure security and compliance, and unlock its power to grow your business. Why Use Sesame Software? Relational Junction builds, populates, and incrementally refreshes your data automatically. Enhance Data Quality - Convert data from multiple sources into a consistent format – leading to more accurate data, which provides the basis for solid decisions. Gain Insights - Automate the update of information into a central location, you can use your in-house BI tools to build useful reports to avoid costly mistakes. Fixed Price - Avoid high consumption costs with yearly fixed prices and multi-year discounts no matter your data volume.

Palo Alto Networks solutions can be enabled by integrating security data from your enterprise. Rapidly simplify security operations by integrating, transforming, and collecting your enterprise's security information. Access to rich data at cloud native scale enables AI and machine learning. Using trillions of multi-source artifacts, you can significantly improve detection accuracy. Cortex XDR™, the industry's leading prevention, detection, response platform, runs on fully integrated network, endpoint, and cloud data. Prisma™, Access protects applications, remote networks, and mobile users in a consistent way, no matter where they are. All users can access all applications via a cloud-delivered architecture, regardless of whether they are at headquarters, branch offices, or on the road. Combining Panorama™, Cortex™, and Data Lake management creates an affordable, cloud-based log solution for Palo Alto Networks Next-Generation Firewalls. Cloud scale, zero hardware, available anywhere.

Mozart Data is the all-in-one modern data platform for consolidating, organizing, and analyzing your data. Set up a modern data stack in an hour, without any engineering. Start getting more out of your data and making data-driven decisions today.

With your own data shop, create new revenue streams from the data you already have. Narrative focuses on the fundamental principles that make buying or selling data simpler, safer, and more strategic. You must ensure that the data you have access to meets your standards. It is important to know who and how the data was collected. Access new supply and demand easily for a more agile, accessible data strategy. You can control your entire data strategy with full end-to-end access to all inputs and outputs. Our platform automates the most labor-intensive and time-consuming aspects of data acquisition so that you can access new data sources in days instead of months. You'll only ever have to pay for what you need with filters, budget controls and automatic deduplication.

IBM Analytics Engine is an architecture for Hadoop clusters that separates the compute and storage layers. Instead of a permanent cluster of dual-purpose nodes the Analytics Engine allows users store data in an object storage layer like IBM Cloud Object Storage. It also spins up clusters with computing notes as needed. The flexibility, scalability, and maintainability of big-data analytics platforms can be improved by separating compute from storage. With the Apache Hadoop and Apache Spark ecosystems, you can build an ODPi-compliant stack that includes cutting-edge data science tools. Define clusters according to your application's needs. Select the appropriate software pack, version, size, and type of cluster. You can use the cluster for as long as you need and then delete it as soon as the job is finished. Create clusters using third-party packages and analytics libraries. Use IBM Cloud services to deploy workloads such as machine learning.

Apache Druid, an open-source distributed data store, is Apache Druid. Druid's core design blends ideas from data warehouses and timeseries databases to create a high-performance real-time analytics database that can be used for a wide range of purposes. Druid combines key characteristics from each of these systems into its ingestion, storage format, querying, and core architecture. Druid compresses and stores each column separately, so it only needs to read the ones that are needed for a specific query. This allows for fast scans, ranking, groupBys, and groupBys. Druid creates indexes that are inverted for string values to allow for fast search and filter. Connectors out-of-the box for Apache Kafka and HDFS, AWS S3, stream processors, and many more. Druid intelligently divides data based upon time. Time-based queries are much faster than traditional databases. Druid automatically balances servers as you add or remove servers. Fault-tolerant architecture allows for server failures to be avoided.

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.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

Customers can deploy Hadoop clusters in any size using Oracle Big Data Service. VM shapes range from 1 OCPU up to a dedicated bare-metal environment. Customers can choose between high-performance block storage or cost-effective block store, and can grow and shrink their clusters. Create Hadoop-based data lakes quickly to expand or complement customer data warehouses and ensure that all data can be accessed and managed efficiently. The included notebook supports R, Python, and SQL. Data scientists can query, visualize, and transform data to build machine learning models. Transfer customer-managed Hadoop clusters from a managed cloud-based service to improve resource utilization and reduce management costs.

SmartHub Analytics, a dedicated telecom big data analytics platform, enables subscriber-based ROI-driven use case. SmartHub Analytics is designed to encourage data sharing and reuse and optimize business performance. It also delivers analytics at the speed and pace of thought. SmartHub Analytics can eliminate silos and can model, validate, and assess vast amounts of data across TEOCO's solution range, including customers, planning, optimization and service assurance. This includes: customer, planning, optimization and service quality. SmartHub Analytics is an analytics layer that can be used in conjunction with other OSS & BSS solutions. It provides a standalone environment for analytics and has a proven return-on-investment (ROI) that saves operators billions. Our customers enjoy significant cost savings by using prediction-based machine learning algorithms. SmartHub Analytics is at the forefront technology by delivering rapid data analyses.