Software Alternatives, Accelerators & Startups
Register | Login

Spark Streaming VS Utilities Meter Data Management

Compare Spark Streaming VS Utilities Meter Data Management and see what are their differences

Flowlu
All-in-one work management platform for team collaboration. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

Utilities Meter Data Management logo 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.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • Utilities Meter Data Management Landing page
    Landing page //
    2023-05-12

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.

Utilities Meter Data Management features and specs

  • Scalability
    Oracle Utilities Meter Data Management is designed to handle large volumes of data, making it scalable for utilities of various sizes.
  • Integration Capabilities
    It offers strong integration capabilities with other utility applications and systems, facilitating seamless data exchange.
  • Advanced Analytics
    Provides powerful analytics tools to enable utilities to gain insights from meter data, helping in decision-making and operational efficiency.
  • Automation
    Automates many data management processes, reducing the need for manual intervention and minimizing errors.
  • Regulatory Compliance
    Supports compliance with regulatory requirements by ensuring accurate and reliable data management and reporting.

Possible disadvantages of Utilities Meter Data Management

  • Complexity
    The system can be complex to implement and configure, often requiring specialized knowledge and resources.
  • Cost
    Investment in Oracle Utilities Meter Data Management can be costly in terms of both software and the necessary infrastructure.
  • Customization Limitations
    Some users may find the customization options limited when compared to niche or highly specialized solutions.
  • Training Requirements
    Employees may need extensive training to effectively use and manage the software, which can be time-consuming.
  • Dependency on Vendor
    Reliance on Oracle for updates and support may lead to delays or constraints in addressing specific organizational needs promptly.

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

Utilities Meter Data Management videos

No Utilities Meter Data Management videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Spark Streaming and Utilities Meter Data Management)

Spark Streaming

Utilities Meter Data Management

Stream Processing
60 60%
Stream Processing
40% 40
Project Management
0 0%
Project Management
100% 100
Data Management
100 100%
Data Management
0% 0
Energy And Utilities Vertical Software
0 0%
Energy And Utilities Vertical Software
100% 100

User comments

Share your experience with using Spark Streaming and Utilities Meter Data Management. For example, how are they different and which one is better?
Log in or Post with

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.

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 / 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

Utilities Meter Data Management mentions (0)

We have not tracked any mentions of Utilities Meter Data Management yet. Tracking of Utilities Meter Data Management recommendations started around Mar 2021.

What are some alternatives?

When comparing Spark Streaming and Utilities Meter Data Management, you can also consider the following products

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

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

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

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

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

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.

Loading...