Real-time Big Data Platform

Leverage real-time analytics and IoT integration to get insights faster than ever.

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Start using Spark Streaming today

Unleash the potential of real-time analytics and IoT integration by leveraging the power of Spark Streaming and machine learning. Talend Real-Time Big Data integration generates native code that can be deployed on-premises, in your cloud, or in the Talend-managed cloud, so you can start working with Apache Spark, Spark MLlib, and Spark Streaming today.

Real-Time Big Data Platform Features

License and Support

  • Subscription license with warranty and indemnification
  • Available as cloud service and downloadable software

Design and Productivity Tools

  • Generates native MapReduce and Spark batch code
  • Generates native Spark Streaming code
  • Visual mapping for complex JSON, XML, and EDI on Spark
  • Spark and MapReduce job designer
  • Serverless Spark processing through Databricks and Qubole
  • Dynamic distribution support
  • Hadoop job scheduler with YARN
  • Hadoop security for Kerberos
  • Ingestion, loading, and unloading data into a data lake
  • Enterprise Messaging (JMS, ActiveMQ, AMQP)
  • Eclipse-based developer tooling and job designer
  • Team collaboration with shared repository
  • Continuous integration / Continuous delivery
  • Audit, job compare, impact analysis, testing, debugging, and tuning
  • Metadata bridge for metadata import/export and centralized metadata management
  • Distant run and parallelization
  • Dynamic schema, re-usable joblets, and reference projects
  • Repository manager
  • ETL and ELT support
  • Wizards and interactive data viewer
  • Versioning
  • Change data capture (CDC)
  • Automatic documentation
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Data Quality and Governance

  • Data profiling and analytics with graphical charts and drill-down data
  • Automate data quality error resolution and enforce rules
  • Data cleansing and masking
  • Data quality portal with monitoring, reporting, and dashboards
  • Semantic discovery with automatic detection of patterns
  • Comprehensive survivorship
  • Data sampling
  • Enrichment, harmonization, fuzzy matching, and de-duplication
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Connectors

  • Cloud: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and more
  • Supported big data distributions: Amazon EMR, Azure HDInsight, Cloudera, Google Dataproc, Hortonworks, MapR
  • Serverless: Cloudera Altus, Databricks, Qubole
  • Spark MLlib (classification, clustering, recommendation, regression)
  • NoSQL: Cassandra, Couchbase, DynamoDB, MongoDB, Neo4j, and more
  • High-Speed messaging components (Kafka, Kinesis, Flume) 
  • RDBMS: Oracle, Teradata, Microsoft SQL server, and more
  • SaaS: Marketo, Salesforce, NetSuite, and more
  • Packaged Apps: SAP, Microsoft Dynamics, Sugar CRM, and more
  • Technologies: Dropbox, Box, SMTP, FTP/SFTP, LDAP, and more
  • Optional 3rd-party address validation services
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Components

  • Hadoop components: HDFS, Hbase, Hive, Pig, Sqoop
  • File management: open, move, compress, decompress without scripting
  • Control and orchestrate data flows and data integrations with master jobs
  • Map, aggregate, sort, enrich, and merge data
  • Standard support: REST, SOAP, OpenID Connect, OAuth, SAML, STS, WSDL, SWAGGER, and more
  • Transports/protocols support: HTTP, JMS, MQTT, AMQP, UDP, Apache Kafka, WebSphere MQ, and more
  • Enterprise Integration Patterns for service mediation, routing, and messaging
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Data Preparation and Stewardship

  • Import, export, and combine data from any database, Excel or CSV file
  • Export to Tableau
  • Self-service on-demand access to sanctioned datasets
  • Share data preparations and datasets
  • Run preparations on Apache Beam*
  • Auto-discovery, standardization, auto-profiling, smart suggestions, and data visualization
  • Auto-discovery, standardization, and auto-profiling of custom semantic types
  • Smart and selective sampling and full-runs
  • Data tracking and masking with role-based security
  • Cleansing and enrichment functions
  • Data Stewardship App for data curation and certification
  • Define data models, data semantics and profile data accordingly
  • Define data models, data semantics and profile data accordingly. Define and apply rules (survivorship, mass updates)
  • Define and apply rules (survivorship, mass updates)
  • Merge and match data, resolve data errors, and arbitrate on data (classification and certification)
  • Merge and match data, resolve data errors, and arbitrate on data (classification and certification)
  • Orchestrate and collaborate on activities in campaigns
  • Orchestrate and collaborate on activities in campaigns
  • Define user roles, workflows and priorities, assign and delegate tasks, tag and comment
  • Define user roles, workflows and priorities, assign and delegate tasks, tag and comment
  • Embed governance and stewardship in data integration flows and manage rejects
  • Embed governance and stewardship in data integration flows and manage rejects
  • Embed human certification and error resolution into MDM processes
  • Embed human certification and error resolution into MDM processes
  • Take matching decisions that cannot be processed automatically
  • Take matching decisions that cannot be processed automatically
  • De-duplicate data at scale with machine learning
  • De-duplicate data at scale with machine learning
  • Audit and track data error resolution actions. Monitor progress of campaigns. Undo/redo based on business needs
  • Audit and track data error resolution actions. Monitor progress of campaigns. Undo/redo based on business needs
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Management and Monitoring

  • High availability, load balancing, failover for jobs
  • Deployment manager and team collaboration
  • Manage users, groups, roles, projects, and licenses
  • Amazon EC2 lifecycle control
  • Execution plan, time, and event-based scheduler
  • Check points, error recovery
  • Context management (dev, QA, prod)
  • Activity monitoring
  • Log server with dashboard
  • Optional Admin user add-on*
  • Engine clusters*
  • Job execution log history (2 months for Entry products, unlimited for Platforms*
  • Environments (2 for Entry products, unlimited for Platforms)*
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Big Data Quality

  • Data cleansing, profiling, masking, parsing, and matching on Spark and Hadoop
  • Data cleansing, profiling, masking, parsing, and matching on Spark and Hadoop (Hybrid and Elastic only)
  • Machine learning for data matching and deduplication
  • Machine learning for data matching and deduplication (Hybrid and Elastic only)
  • Support for Cloudera Navigator and Apache Atlas
  • HDFS file profiling
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ESB Management             (downloadable software version)

  • JMX monitoring, service activity monitoring
  • System monitoring
  • Visibility into live statistics of message flow activity
  • Integrated artifact repository
  • Centralized event logging service, provision service
  • HypericHQ plug-ins
  • Job conductor
  • Identity management and authorization
  • Web services high availability
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Agile Application Integration (downloadable software version)

  • Drag-and-drop route, data, and web/REST services creation
  • WS policy-based web services security
  • Deliver and route messages and events based on Enterprise Integration Patterns (EIPs)
  • Service locator and registry
  • Service creation, mediation, and simulation
  • Functional, load, and security web services testing
  • Command line and scripting tools
  • XML key management specification (XKMS)
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ESB Packaging and Deployment (downloadable software version)

  • ESB deployment container flexibility
  • OSGI packaging deployed to Talend Runtime
  • Spring Boot packaging deployed as a microservice

Advanced Data Profiling

  • Fraud pattern detection using Benford Law
  • Column set analysis
  • Advanced matching analysis
  • Time column correlation analysis
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Talend Named a Leader Again!

Talend has been named a leader in the 2018 Gartner Magic Quadrant for Data Integration Tools for the 3rd year in a row.

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Accelerate the move to cloud and real-time

Easily convert an existing batch integration job into real-time with a few clicks without having to learn Spark code. Talend Studio comes with 900+ pre-built connectors and components, including over 100 Spark components. Work with Hadoop, cloud SaaS and storage, databases, and NoSQL today.

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Accelerate the Move to Cloud and Real-time

Run real-time big data projects at scale

Only Talend takes advantage of Spark, Spark Streaming, Hadoop, NoSQL, and cloud by generating native code. Ingest, process, enrich, and cleanse data within your big data framework to leverage its full power and scale whether on-premises or in the cloud. Stream data directly into your data lake using Apache Kafka, Amazon Kinesis, Google Pub/Sub, and more.

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How to Fast Track Your Real-Time Big Data Project

Unleash the potential of advanced analytics

Talend puts sophisticated machine learning technologies into the hands of data engineers, so they can easily create smarter data pipelines. Discover, predict, and respond to business opportunities and threats in real-time by leveraging Spark in-memory machine processing and out-of-the-box, drag-and-drop Spark machine learning (MLlib) components, including Classification, Clustering, Decision Tree, KMeans, Prediction, Random Forrest, Recommendation, and Regression.

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Start Using Machine Learning Today

Talend Named a Leader.

We are a leader in the Forrester Wave™: Big Data Fabric, Q2 2018. We earned the highest scores of any vendor in the report in both the Current Offering and Strategy categories.

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Subscription Pricing

Talend significantly lowers the upfront cost and total cost of ownership by charging per user.
Start where you are and scale as you need to.

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