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The PI System VS Spark Streaming

Compare The PI System VS Spark Streaming and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

The PI System logo The PI System

With the PI System, OSIsoft customers have reduced costs, opened new revenue streams, extended equipment life, increased production capacity, and more.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • The PI System Landing page
    Landing page //
    2023-09-25
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

The PI System features and specs

  • Real-time Data Collection
    The PI System allows companies to capture and visualize real-time data from various sources, enabling quick decision-making and operational efficiency.
  • High Scalability
    The system is designed to handle vast amounts of data, making it suitable for both small-scale and large-scale industrial applications.
  • Integration Capabilities
    The PI System can integrate with numerous third-party applications and systems, enhancing its flexibility and utility in diverse industrial environments.
  • Data Analytics and Reporting
    The system includes robust analytics and reporting tools that help users derive actionable insights from the collected data.
  • Security Features
    The PI System offers comprehensive security features to protect sensitive data, which is crucial for industrial applications.

Possible disadvantages of The PI System

  • High Cost
    The initial investment and ongoing costs for the PI System can be significant, which may not be feasible for all organizations.
  • Complex Implementation
    Implementing the PI System can be complex and time-consuming, requiring specialized knowledge and skills.
  • Maintenance and Support
    Ongoing maintenance and support can be resource-intensive, requiring dedicated personnel and continuous effort.
  • Learning Curve
    There can be a steep learning curve for new users, which might require extensive training to fully leverage the system's capabilities.
  • Dependence on Continuous Connectivity
    The system's performance and reliability are highly dependent on continuous and stable network connectivity, which might not always be guaranteed.

Spark Streaming features and specs

  • Scalability
    Spark Streaming is highly scalable and can handle large volumes of data by distributing the workload across a cluster of machines. It leverages Apache Spark's capabilities to scale out easily and efficiently.
  • Integration
    It integrates seamlessly with other components of the Spark ecosystem, such as Spark SQL, MLlib, and GraphX, allowing for comprehensive data processing pipelines.
  • Fault Tolerance
    Spark Streaming provides fault tolerance by using Spark's micro-batching approach, which allows the system to recover data in case of a failure.
  • Ease of Use
    Spark Streaming provides high-level APIs in Java, Scala, and Python, making it relatively easy to develop and deploy streaming applications quickly.
  • Unified Platform
    It provides a unified platform for both batch and streaming data processing, allowing reuse of code and resources across different types of workloads.

Possible disadvantages of Spark Streaming

  • Latency
    Spark Streaming operates on a micro-batch processing model, which introduces latency compared to real-time processing. This may not be suitable for applications requiring immediate responses.
  • Complexity
    While it integrates well with other Spark components, building complex streaming applications can still be challenging and may require expertise in distributed systems and stream processing concepts.
  • Resource Management
    Efficiently managing cluster resources and tuning the system can be difficult, especially when dealing with variable workload and ensuring optimal performance.
  • Backpressure Handling
    Handling backpressure effectively can be a challenge in Spark Streaming, requiring careful management to prevent resource saturation or data loss.
  • Limited Windowing Support
    Compared to some stream processing frameworks, Spark Streaming has more limited options for complex windowing operations, which can restrict some advanced use cases.

Analysis of The PI System

Overall verdict

  • Yes, the PI System is considered a good choice for organizations needing comprehensive real-time data solutions. Its reliability, scalability, and extensive integration capabilities make it a popular choice in industries such as manufacturing, energy, and utilities.

Why this product is good

  • The PI System by OSIsoft is highly regarded for its real-time data management capabilities. It's particularly noted for its ability to collect, store, and analyze large volumes of data from various sensors and devices in industrial settings. The system provides powerful visualization tools, robust data integration options, and scalable architecture, which make it a valuable tool for driving operational efficiency and informed decision-making.

Recommended for

    The PI System is recommended for industries and businesses that require real-time data analysis and decision-making, such as energy and utilities, oil and gas, manufacturing, pharmaceuticals, and any operation that benefits from industrial internet of things (IIoT) solutions.

The PI System videos

+ Add

What does PI System do?

Spark Streaming videos

+ Add

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

Category Popularity

0-100% (relative to The PI System and Spark Streaming)

The PI System

Spark Streaming

Project Management
100 100%
Project Management
0% 0
Stream Processing
52 52%
Stream Processing
48% 48
Energy And Utilities Vertical Software
100 100%
Energy And Utilities Vertical Software
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

Share your experience with using The PI System and Spark Streaming. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Spark Streaming seems to be more popular. It has been mentiond 5 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

The PI System mentions (0)

We have not tracked any mentions of The PI System yet. Tracking of The PI System recommendations started around Mar 2021.

Spark Streaming mentions (5)

  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / about 2 months ago
  • Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot
    Apache Spark Streaming: Offers micro-batch processing, suitable for high-throughput scenarios that can tolerate slightly higher latency. https://spark.apache.org/streaming/. - Source: dev.to / 10 months ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / over 1 year ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 2 years ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing The PI System and Spark Streaming, you can also consider the following products

Oracle DataRaker - Oracle DataRaker unlocks smart meter data and transforms it into compelling, quantifiable, and actionable results with low upfront investment and risk.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Utilities Meter Data Management - Oracle's Applications for Meter Data Management helps utilities to support the loading, validation, editing, and estimation (VEE) of meter data.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

ATLAS Energy Monitoring System - AtlasEVO Energy Management & Energy Monitoring Systems. Collect and analyse energy usage data (electric, gas, water etc) from any number of metering points.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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