Best Real-Time Data Streaming Tools in New Zealand - Page 2
View:
Compare the Top Real-Time Data Streaming Tools in New Zealand as of November 2024 - Page 2
Sort By:
Need help deciding?
Talk to one of our software experts for free. They will help you select the best software for your business.
-
1
TapData
TapData
CDC-based live data platform for heterogeneous database replication, real-time data integration, or building a real-time data warehouse. By using CDC to sync production line data stored in DB2 and Oracle to the modern database, TapData enabled an AI-augmented real-time dispatch software to optimize the semiconductor production line process. The real-time data made instant decision-making in the RTD software a possibility, leading to faster turnaround times and improved yield. As one of the largest telcos, customer has many regional systems that cater to the local customers. By syncing and aggregating data from various sources and locations into a centralized data store, customers were able to build an order center where the collective orders from many applications can now be aggregated. TapData seamlessly integrates inventory data from 500+ stores, providing real-time insights into stock levels and customer preferences, enhancing supply chain efficiency. -
2
Amazon Kinesis
Amazon
Easily collect, process, and analyze video and data streams in real time. Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin. Amazon Kinesis enables you to ingest, buffer, and process streaming data in real-time, so you can derive insights in seconds or minutes instead of hours or days. -
3
Informatica Data Engineering Streaming
Informatica
AI-powered Informatica Data Engineering Streaming enables data engineers to ingest, process, and analyze real-time streaming data for actionable insights. Advanced serverless deployment option with integrated metering dashboard cuts admin overhead. Rapidly build intelligent data pipelines with CLAIRE®-powered automation, including automatic change data capture (CDC). Ingest thousands of databases and millions of files, and streaming events. Efficiently ingest databases, files, and streaming data for real-time data replication and streaming analytics. Find and inventory all data assets throughout your organization. Intelligently discover and prepare trusted data for advanced analytics and AI/ML projects. -
4
Apache Storm
Apache Software Foundation
Apache Storm is a free and open source distributed realtime computation system. Apache Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Apache Storm is simple, can be used with any programming language, and is a lot of fun to use! Apache Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Apache Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Apache Storm integrates with the queueing and database technologies you already use. An Apache Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial. -
5
Apache NiFi
Apache Software Foundation
An easy to use, powerful, and reliable system to process and distribute data. Apache NiFi supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. Some of the high-level capabilities and objectives of Apache NiFi include web-based user interface, offering a seamless experience between design, control, feedback, and monitoring. Highly configurable, loss tolerant, low latency, high throughput, and dynamic prioritization. Flow can be modified at runtime, back pressure, data provenance, track dataflow from beginning to end, designed for extension. Build your own processors and more. Enables rapid development and effective testing. Secure, SSL, SSH, HTTPS, encrypted content, and much more. Multi-tenant authorization and internal authorization/policy management. NiFi is comprised of a number of web applications (web UI, web API, documentation, custom UI's, etc). So, you'll need to set up your mapping to the root path. -
6
Built on open-source Apache Kafka, IBM® Event Streams is an event-streaming platform that helps you build smart apps that can react to events as they happen. Event Streams is based on years of IBM operational expertise gained from running Apache Kafka event streams for enterprises. This makes Event Streams ideal for mission-critical workloads. With connectors to a wide range of core systems and a scalable REST API, you can extend the reach of your existing enterprise assets. Rich security and geo-replication aids disaster recovery. Take advantage of IBM productivity tools and use the CLI to ensure best practices. Replicate data between Event Streams deployments in a disaster-recovery situation.
-
7
Spark Streaming
Apache Software Foundation
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability. -
8
Samza
Apache Software Foundation
Samza allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka. Battle-tested at scale, it supports flexible deployment options to run on YARN or as a standalone library. Samza provides extremely low latencies and high throughput to analyze your data instantly. Scales to several terabytes of state with features like incremental checkpoints and host-affinity. Samza is easy to operate with flexible deployment options - YARN, Kubernetes or standalone. Ability to run the same code to process both batch and streaming data. Integrates with several sources including Kafka, HDFS, AWS Kinesis, Azure Eventhubs, K-V stores and ElasticSearch. -
9
Apache Beam
Apache Software Foundation
The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads. Beam reads your data from a diverse set of supported sources, no matter if it’s on-prem or in the cloud. Beam executes your business logic for both batch and streaming use cases. Beam writes the results of your data processing logic to the most popular data sinks in the industry. A simplified, single programming model for both batch and streaming use cases for every member of your data and application teams. Apache Beam is extensible, with projects such as TensorFlow Extended and Apache Hop built on top of Apache Beam. Execute pipelines on multiple execution environments (runners), providing flexibility and avoiding lock-in. Open, community-based development and support to help evolve your application and meet the needs of your specific use cases. -
10
Apache Flume
Apache Software Foundation
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault-tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms. It uses a simple extensible data model that allows for online analytic applications. The Apache Flume team is pleased to announce the release of Flume 1.8.0. Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data. -
11
ksqlDB
Confluent
Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications. Make your data immediately actionable by continuously processing streams of data generated throughout your business. ksqlDB’s intuitive syntax lets you quickly access and augment data in Kafka, enabling development teams to seamlessly create real-time innovative customer experiences and fulfill data-driven operational needs. ksqlDB offers a single solution for collecting streams of data, enriching them, and serving queries on new derived streams and tables. That means less infrastructure to deploy, maintain, scale, and secure. With less moving parts in your data architecture, you can focus on what really matters -- innovation. -
12
VeloDB
VeloDB
Powered by Apache Doris, VeloDB is a modern data warehouse for lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within seconds. Storage engine with real-time upsert、append and pre-aggregation. Unparalleled performance in both real-time data serving and interactive ad-hoc queries. Not just structured but also semi-structured data. Not just real-time analytics but also batch processing. Not just run queries against internal data but also work as a federate query engine to access external data lakes and databases. Distributed design to support linear scalability. Whether on-premise deployment or cloud service, separation or integration of storage and compute, resource usage can be flexibly and efficiently adjusted according to workload requirements. Built on and fully compatible with open source Apache Doris. Support MySQL protocol, functions, and SQL for easy integration with other data tools. -
13
Baidu AI Cloud Stream Computing
Baidu AI Cloud
Baidu Stream Computing (BSC) provides real-time streaming data processing capacity with low delay, high throughput and high accuracy. It is fully compatible with Spark SQL; and can realize the logic data processing of complicated businesses through SQL statement, which is easy to use; provides users with full life cycle management for the streaming-oriented computing jobs. Integrate deeply with multiple storage products of Baidu AI Cloud as the upstream and downstream of stream computing, including Baidu Kafka, RDS, BOS, IOT Hub, Baidu ElasticSearch, TSDB, SCS and others. Provide a comprehensive job monitoring indicator, and the user can view the monitoring indicators of the job and set the alarm rules to protect the job. -
14
Arroyo
Arroyo
Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes. -
15
3forge
3forge
Your enterprise's issues may be complex. That doesn't mean building the solution has to be. 3forge is the highly-flexible, low-code platform that empowers enterprise application development in record time. Reliability? Check. Scalability? That too. Deliverability? In record time. Even for the most complex work flows and data sets. With 3forge, you no longer have to choose. Data integration, virtualization, processing, visualization, and workflows all living in one place - solving the world's most complex real-time streaming data challenges. 3forge provides award-winning technology that enables developers to deploy mission-critical applications in record time. Experience the difference of real-time data and zero latency with 3forge's focus on data integration, virtualization, processing, and visualization. -
16
Google Cloud Dataflow
Google
Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization. -
17
Azure Stream Analytics
Microsoft
Discover Azure Stream Analytics, the easy-to-use, real-time analytics service that is designed for mission-critical workloads. Build an end-to-end serverless streaming pipeline with just a few clicks. Go from zero to production in minutes using SQL—easily extensible with custom code and built-in machine learning capabilities for more advanced scenarios. Run your most demanding workloads with the confidence of a financially backed SLA. -
18
Cloudera DataFlow
Cloudera
Cloudera DataFlow for the Public Cloud (CDF-PC) is a cloud-native universal data distribution service powered by Apache NiFi that lets developers connect to any data source anywhere with any structure, process it, and deliver to any destination. CDF-PC offers a flow-based low-code development paradigm that aligns best with how developers design, develop, and test data distribution pipelines. With over 400+ connectors and processors across the ecosystem of hybrid cloud services—including data lakes, lakehouses, cloud warehouses, and on-premises sources—CDF-PC provides indiscriminate data distribution. These data distribution flows can then be version-controlled into a catalog where operators can self-serve deployments to different runtimes. -
19
Apache Flink
Apache Software Foundation
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Apache Flink excels at processing unbounded and bounded data sets. Precise control of time and state enable Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed sized data sets, yielding excellent performance. Flink is designed to work well each of the previously listed resource managers. -
20
Hitachi Streaming Data Platform
Hitachi
Hitachi is a software company based in Japan and offers a software product called Hitachi Streaming Data Platform. Hitachi Streaming Data Platform is Real-Time data streaming software, and includes features such as data enrichment, data wrangling / data prep, multiple data source support, process automation, real-time analysis / reporting, and visualization dashboards. Hitachi Streaming Data Platform offers training via documentation. Hitachi Streaming Data Platform offers phone support support. Some alternative products to Hitachi Streaming Data Platform include Materialize, Apache Flink, and Cloudera DataFlow.
