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Stitch VS Apache Spark

Compare Stitch VS Apache Spark and see what are their differences

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Stitch logo Stitch

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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.
  • Stitch Landing page
    Landing page //
    2023-05-10
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Stitch features and specs

  • Ease of use
    Stitch is user-friendly with a simple interface that allows users to set up data integrations quickly without extensive technical knowledge.
  • Wide range of integrations
    Stitch supports a wide variety of data sources and destinations, making it versatile for different data needs.
  • Scalability
    Stitch is built to handle large data volumes, making it suitable for growing businesses with increasing data requirements.
  • Transparent pricing
    Stitch offers clear and straightforward pricing plans based on the volume of data, allowing businesses to predict costs easily.
  • Flexibility
    Users can customize their data integrations with options to filter and select specific fields for extraction, transformation, and loading.

Possible disadvantages of Stitch

  • Limited data transformation
    Stitch provides basic transformation capabilities. Users may need additional tools for complex data transformations.
  • Cost for high-volume users
    While pricing is transparent, costs can add up for users with high data volumes, potentially making it expensive.
  • Occasional latency
    Some users experience delays in data syncing, which may be challenging for real-time data needs.
  • Support
    Support services can be limited, especially for lower-tier plans, which might be an issue for users requiring immediate assistance.
  • Limited customization
    Although it offers flexibility, some users may find the customization options insufficient for very specific or advanced use cases.

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 Stitch

Overall verdict

  • Overall, Stitch is regarded as a good and reliable ETL tool, especially praised for its ease of use and efficient data handling capabilities, making it a popular option among businesses looking to streamline their data pipeline processes.

Why this product is good

  • Stitch (stitchdata.com) is considered a strong choice for data integration needs due to its ability to efficiently extract, transform, and load (ETL) data from various sources into data warehouses. It offers a user-friendly interface, supports over 100 integrations, and provides scalable solutions for businesses of varying sizes. Its pay-as-you-go pricing model and cloud-native platform make it accessible and flexible for many users.

Recommended for

  • Small to medium-sized businesses looking for a cost-effective data integration solution.
  • Organizations that need to integrate data from multiple sources rapidly.
  • Data teams that prefer a tool with a straightforward, intuitive interface.
  • Companies leveraging cloud data warehouses like Amazon Redshift, Google BigQuery, or Snowflake.

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.

Stitch videos

+ Add

Let's Talk About: Stitch! The Anime - A Review

More videos:

  • Review - Lilo and Stitch - Disney's Unusual Masterpiece
  • Review - Let's Talk About: Stitch and Ai - A Review

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 Stitch and Apache Spark)

Stitch

Apache Spark

Data Integration
100 100%
Data Integration
0% 0
Databases
0 0%
Databases
100% 100
ETL
100 100%
ETL
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 Stitch and Apache Spark

Stitch Reviews

Best ETL Tools: A Curated List
Stitch is a SaaS-based batch ELT tool originally developed as part of the Singer open-source project within RJMetrics. After its acquisition by Talend in 2018, Stitch has continued to provide a straightforward, cloud-native solution for automating data extraction and loading into data warehouses. Although branded as an ETL tool, Stitch operates primarily as a batch ELT...
Source: estuary.dev
Best Affordable Alternatives to Supermetrics
Stitch is a powerful ETL tool since it can be easily customized and is safe from outside interference. With their open-source code, you may use them with any tool, not only the ones they support. They also guarantee HIPAA and GDPR compliance. Making a decision might be crucial for businesses, particularly in the health industry.
Source: adsbot.co
Top 11 Fivetran Alternatives for 2024
Stitch is a SaaS-based batch ELT tool developed from the Singer open-source project. It was initially created within RJMetrics, and when Magento acquired RJMetrics in 2016, Stitch spun off as an independent company. In 2017, Stitch made contributions to the Singer open-source project, and in 2018, it was acquired by Talend. Currently, Stitch is utilized by over 3,000...
Source: estuary.dev
10 Best ETL Tools (October 2023)
An open-source ELT (extract, load, transform) data integration platform, Stitch is one more excellent choice. Similar to Talend, Stitch offers paid service tiers for more advanced use cases and larger numbers of data sources. Stitch was actually acquired by Talend in 2018.
Source: www.unite.ai
15+ Best Cloud ETL Tools
Stitch Data is an efficient, cloud-based ETL platform that enables businesses to seamlessly transfer their structured and unstructured data from various sources into data warehouses and data lakes. It provides tools for transforming data within the data warehouse or via external engines like Spark and MapReduce. As a part of Talend Data Fabric, Stitch Data focuses on...
Source: estuary.dev

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.

Stitch mentions (0)

We have not tracked any mentions of Stitch yet. Tracking of Stitch 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 / about 1 month 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 1 month 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
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What are some alternatives?

When comparing Stitch 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.

Skyvia - Free cloud data platform for data integration, backup & management

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

Xplenty - Xplenty is the #1 SecurETL - allowing you to build low-code data pipelines on the most secure and flexible data transformation platform. No longer worry about manual data transformations. Start your free 14-day trial now.

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

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