Software Alternatives, Accelerators & Startups
Register | Login

Oracle DataRaker VS Apache Spark

Compare Oracle DataRaker VS Apache Spark and see what are their differences

VisualVisitor
Consent-Based Identification of the Person Visiting Your Website Including First Name, Last Name, Email & 37 Other Data Points. Identify and Influence Your Engaged Website Visitors into Sales-Ready Leads – Before You Commit a Single Working Hour. 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

Oracle DataRaker logo Oracle DataRaker

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

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.
  • Oracle DataRaker Landing page
    Landing page //
    2023-02-09
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Oracle DataRaker features and specs

  • Scalability
    Oracle DataRaker is a highly scalable platform that can handle large volumes of data, making it suitable for utilities with extensive customer bases.
  • Advanced Analytics
    It offers advanced analytics capabilities that help utilities gain deeper insights into their operations, enabling data-driven decision-making.
  • Integration
    DataRaker seamlessly integrates with other Oracle utilities applications and third-party systems, ensuring streamlined data flow and enhanced functionality.
  • Cloud-Based
    Being cloud-based, it reduces the need for on-premises infrastructure and simplifies maintenance and updates.
  • Real-Time Monitoring
    Provides real-time monitoring and analytics, allowing utilities to quickly identify and respond to issues.

Possible disadvantages of Oracle DataRaker

  • Cost
    Oracle DataRaker can be expensive, which might be a barrier for smaller utilities or those with limited budgets.
  • Complexity
    The platform can be complex to implement and manage, requiring skilled personnel for effective use and management.
  • Dependency on Cloud
    Being dependent on the cloud can be a disadvantage for utilities operating in regions with limited internet connectivity.
  • Customization
    Customization options may be limited, potentially leading to challenges when specific needs or requirements are not met.
  • Training and Onboarding
    Training and onboarding for new users might be necessary due to the platform’s complexity, adding to initial deployment timeframes.

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.

Oracle DataRaker videos

+ Add

Analyze and predict transformer failure with Oracle DataRaker

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

Category Popularity

0-100% (relative to Oracle DataRaker and Apache Spark)

Oracle DataRaker

Apache Spark

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

User comments

Share your experience with using Oracle DataRaker and Apache Spark. 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 Oracle DataRaker and Apache Spark

Oracle DataRaker Reviews

We have no reviews of Oracle DataRaker yet.
Be the first one to post

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

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.

Oracle DataRaker mentions (0)

We have not tracked any mentions of Oracle DataRaker yet. Tracking of Oracle DataRaker recommendations started around Mar 2021.

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 / 18 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 / 20 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

What are some alternatives?

When comparing Oracle DataRaker and Apache Spark, you can also consider the following products

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

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

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.

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

GENERIS Platform - Meter Data Management

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

Loading...