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Entity Framework VS Spark Streaming

Compare Entity Framework VS Spark Streaming and see what are their differences

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Entity Framework logo Entity Framework

See Comparison of Entity Framework vs NHibernate.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Entity Framework Landing page
    Landing page //
    2023-08-18
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Entity Framework features and specs

  • Productivity
    Entity Framework automates database-related code generation, reducing the amount of boilerplate code developers must write and maintain. This allows developers to work more efficiently and focus more on business logic.
  • Abstraction
    It abstracts the database interaction details, enabling developers to work with higher-level .NET objects instead of raw SQL queries, resulting in clearer and more manageable code.
  • Code First Approach
    This allows developers to define their database schema using C# classes, making it easy to evolve the database alongside the codebase using migrations.
  • Support for Multiple Databases
    Entity Framework supports a wide range of relational databases, including SQL Server, PostgreSQL, SQLite, and MySQL, providing flexibility and choice to the developers.
  • Change Tracking
    It provides automatic change tracking of entity objects, simplifying the process of updating data in the database without manually tracking object changes.

Possible disadvantages of Entity Framework

  • Performance Overhead
    The abstraction layer can lead to performance overhead compared to plain SQL queries, as the generated queries might not be as optimized as handcrafted SQL.
  • Complexity
    For simple or small applications, the complexity introduced by using an ORM like Entity Framework might be unnecessary and could complicate the architecture.
  • Learning Curve
    Developers need to learn the specific concepts and configurations of Entity Framework, which can be time-consuming compared to traditional database access methodologies.
  • Debugging Difficulty
    Debugging issues can be more challenging because of the abstraction, making it sometimes difficult to trace the exact query being executed and pinpoint performance bottlenecks.
  • Limited SQL Features
    While Entity Framework supports a wide range of SQL functionalities, there are advanced features specific to certain databases that may not be fully supported or could require custom implementation.

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.

Entity Framework videos

+ Add

Entity Framework Best Practices - Should EFCore Be Your Data Access of Choice?

More videos:

  • Tutorial - Entity Framework 6 Tutorial: Learn Entity Framework 6 from Scratch
  • Review - Getting the best out of Entity Framework Core - Jon P Smith

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 Entity Framework and Spark Streaming)

Entity Framework

Spark Streaming

Web Frameworks
100 100%
Web Frameworks
0% 0
Stream Processing
0 0%
Stream Processing
100% 100
Development
100 100%
Development
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

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Social recommendations and mentions

Based on our record, Entity Framework should be more popular than Spark Streaming. It has been mentiond 15 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.

Entity Framework mentions (15)

  • Create a Simple .NET Workflow App From Scratch – Your Ultimate Guide
    For the simplicity we will use MSSQLProvider to fetch the data from the database. This class has basic functionality, if you want to create complex database queries, for example JOIN, you'd better use something like Entity Framework. - Source: dev.to / about 1 year ago
  • Entity Framework Core in .NET 7 7️⃣
    I only wanted to give a simple preview of what can be done with Entity Framework, but if this is something that interests you and you want to go further in-depth with all the possibilities, I recommend checking out the official docs where you can also find a great tutorial which will guide you through building your very own .NET Core web application. - Source: dev.to / almost 2 years ago
  • Got an internship, need help with .NET
    Entity Framework documentation hub - Entity Framework is a modern object-relation mapper that lets you build a clean, portable, and high-level data access layer with .NET (C#) across a variety of databases, including SQL Database (on-premises and Azure), SQLite, MySQL, PostgreSQL, and Azure Cosmos DB. It supports LINQ queries, change tracking, updates, and schema migrations. Source: almost 2 years ago
  • How to create a "Database Project" that can be used across multiple .NET apps?
    You can create the DAL using your existing code or start using a Object Relational Mapper like Entity Framework which will do a lot of the work for you, check this out here: https://learn.microsoft.com/en-us/ef/ also check out LINQ. Source: about 2 years ago
  • Website with Database. use C#
    And, possibly (not strictly speaking necessary but very useful) Entity framework as a backend part of it. Source: about 2 years ago
View more

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 1 month 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 / 9 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 Entity Framework and Spark Streaming, you can also consider the following products

Sequelize - Provides access to a MySQL database by mapping database entries to objects and vice-versa.

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

SQLAlchemy - SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.

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

Hibernate - Hibernate an open source Java persistence framework project.

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

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