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Hevo Data VS Apache Spark

Compare Hevo Data VS Apache Spark and see what are their differences

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  3. » Reviews
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Hevo Data logo Hevo Data

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. Get near real-time data pipelines for reporting and analytics up and running in just a few minutes. Try Hevo for Free today!

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.
  • Hevo Data Landing page
    Landing page //
    2023-02-18

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows that result in a saving of ~10 hours of engineering time/week and 10x faster reporting, analytics, and decision making.

The platform supports 100+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 500 data-driven companies spread across 35+ countries trust Hevo for their data integration needs.

Try Hevo today and get your fully managed data pipelines up and running in just a few minutes.

  • Apache Spark Landing page
    Landing page //
    2021-12-31

Hevo Data features and specs

  • Data Extraction and Loading
    Integrate and manage data from 100+ sources
  • Data Transformation
    Run pre-load data transformation
  • Customer Support
    24/7 Live chat support

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.

Analysis of Hevo Data

Overall verdict

  • Hevo Data is generally considered a good choice for businesses that require efficient and reliable data integration solutions. Its features and performance make it a viable option for organizations looking to enhance their data workflows.

Why this product is good

  • Hevo Data is often praised for its user-friendly interface, easy setup process, and reliable performance in data integration. It offers automated data pipelines that help reduce manual effort and improve data accuracy, making it a popular choice among businesses looking to streamline their data operations. Additionally, it supports numerous data sources and destinations, offering flexibility and scalability to accommodate growing data needs.

Recommended for

    Hevo Data is recommended for businesses of all sizes that are seeking an easy-to-use platform for automating their data integration processes. It is particularly beneficial for teams that may not have extensive technical expertise but still need to manage complex data environments effectively. Companies looking for a scalable solution to handle real-time data streaming and transformation will also find Hevo Data beneficial.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Hevo Data videos

+ Add

Getting Started with Hevo - An Overview

More videos:

  • Tutorial - Load Data from AWS S3 to Data Warehouse
  • Tutorial - ETL REST API Data to a Data Warehouse
  • Demo - Data Transformations on Hevo

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 Hevo Data and Apache Spark)

Hevo Data

Apache Spark

Data Replication
100 100%
Data Replication
0% 0
Databases
0 0%
Databases
100% 100
Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

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

Hevo Data Reviews

Best ETL Tools: A Curated List
Hevo Data is a cloud-based ETL/ELT service that allows users to build data pipelines easily. Launched in 2017, Hevo provides a low-code platform, giving users more control over mapping sources to targets and performing simple transformations using Python scripts or a drag-and-drop editor (currently in Beta). While Hevo is ideal for beginners, it has some limitations compared...
Source: estuary.dev
Top 11 Fivetran Alternatives for 2024
Hevo Data is a no-code SaaS data pipeline platform that started as a cloud service in 2017. Hevo is primarily ELT but has been adding some row-based ETL support.
Source: estuary.dev
15+ Best Cloud ETL Tools
Hevo Data is one of the leading open-source ETL tools. It is a cloud-based, no-code data pipeline solution with ETL functionality for efficient data integration and management across all your systems. It provides easy data collection and reporting capabilities that can help your business ensure that accurate and real-time data is always available.
Source: estuary.dev
Top 14 ETL Tools for 2023
Hevo Data is an ETL data integration platform with over 100 pre-built connectors to databases, cloud storage, and SaaS sources. Users can define their own pre-load transformations in Hevo Data using Python. Hevo Data supports the most popular data warehouse destinations, including Redshift, BigQuery, and Snowflake.
Source: www.integrate.io
Top 10 Fivetran Alternatives - Listing the best ETL tools
‍Hevo Data has ETL, ELT, and reverse-ETL capabilities, and is code-free with integrations to various tools and data warehouses. For non-technical users who want to get up and running with their data, Hevo can help.
Source: weld.app

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 should be more popular than Hevo Data. 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.

Hevo Data mentions (9)

  • Understanding the MLOps Lifecycle
    Some popular tools for data extraction are Airbyte, Fivetran, Hevo Data, and many more. - Source: dev.to / 6 months ago
  • Quick tip: Replicating a MongoDB Atlas database to SingleStoreDB Cloud using Hevo Data
    In a previous article, we used open-source Airbyte to create an ELT pipeline between SingleStoreDB and Apache Pulsar. We have also seen in another article several methods to ingest MongoDB JSON data into SingleStoreDB. In this article, we’ll evaluate a commercial ELT tool called Hevo Data to create a pipeline between MongoDB Atlas and SingleStoreDB Cloud. Switching to SingleStoreDB has many benefits, as described... - Source: dev.to / over 2 years ago
  • Best methods for pulling data from IBM DB2 (AS/400) to Snowflake?
    One of my customers just purchased Precisely to extract from their iSeries machines into Snowflake. Hevo can also do it. Source: over 2 years ago
  • Lowest latency dynamodb to redshift sync?
    I've been looking at Hevo data as well, and they certainly make the setup/maintenance a lot easier, but they have a latency of 5-10 minutes. What's the minimum lowest latency that can be achieved with aws for syncing dynamodb to redshift? Source: almost 3 years ago
  • Best ‘E’TL tools for extracting data from on-prem SQL databases
    Don't decide on something without looking at Hevo - I've used this in two organisations now and can't speak more highly of it. Cheap, super simple to use, and super configurable if you want to get into the nitty gritty. Source: about 3 years ago
View more

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 / about 2 months 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 / about 2 months 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 / 3 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 / 3 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 / 4 months ago
View more

What are some alternatives?

When comparing Hevo Data and Apache Spark, you can also consider the following products

Fivetran - Fivetran offers companies a data connector for extracting data from many different cloud and database sources.

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

Stitch - Consolidate your customer and product data in minutes

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

Improvado.io - Improvado is an ETL platform that extracts data from 300+ pre-built connectors, transforms it, and seamlessly loads the results to wherever you need them. No more Tedious Manual Work, Errors or Discrepancies. Contact us for a demo.

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

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