Software Alternatives, Accelerators & Startups
Register | Login

Apache Spark VS Utilities Meter Data Management

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

DocRaptor
As the only API powered by the Prince HTML-to-PDF engine, DocRaptor provides the best support for complex PDFs with powerful support for headers, page breaks, page numbers, flexbox, watermarks, accessible PDFs, and much more 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

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Utilities Meter Data Management Landing page
    Landing page //
    2023-05-12

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

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.

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

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 Apache Spark and Utilities Meter Data Management)

Apache Spark

Utilities Meter Data Management

Databases
100 100%
Databases
0% 0
Project Management
0 0%
Project Management
100% 100
Big Data
100 100%
Big Data
0% 0
Energy And Utilities Vertical Software
0 0%
Energy And Utilities Vertical Software
100% 100

User comments

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

Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Spark and Utilities Meter Data Management

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Utilities Meter Data Management Reviews

We have no reviews of Utilities Meter Data Management yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Apache Spark seems to be more popular. It has been mentiond 70 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.

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 25 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 27 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

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 Apache Spark and Utilities Meter Data Management, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

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

Hadoop - Open-source software for reliable, scalable, distributed computing

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

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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...