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Apache Spark VS Google Cloud Spanner

Compare Apache Spark VS Google Cloud Spanner and see what are their differences

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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.

Google Cloud Spanner logo Google Cloud Spanner

Google Cloud Spanner is a horizontally scalable, globally consistent, relational database service.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Google Cloud Spanner Landing page
    Landing page //
    2023-09-17

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.

Google Cloud Spanner features and specs

  • Scalability
    Google Cloud Spanner can automatically scale horizontally, providing robust support for large-scale applications. It can handle petabytes of data across millions of instances with ease.
  • Global Distribution
    Spanner enables globally distributed databases with strong consistency and low-latency reads, allowing applications to deliver seamless performance across the globe.
  • Strong Consistency
    Unlike many other distributed databases, Cloud Spanner offers strong transactional consistency, using Google's TrueTime API to ensure precise timestamp ordering that supports ACID transactions.
  • Fully Managed
    Cloud Spanner is a fully managed service, which means Google handles maintenance tasks such as updates, scaling, and provisioning, reducing the operational overhead for users.
  • SQL Support
    It provides support for SQL queries, making it easier for developers and teams familiar with SQL to integrate and manage their data workloads without needing to learn new paradigms.
  • High Availability
    Cloud Spanner is designed for high availability, with built-in redundancy and failover capabilities that ensure continuous operation even in the face of regional outages.

Possible disadvantages of Google Cloud Spanner

  • Cost
    Google Cloud Spanner can be expensive compared to other database solutions, especially for smaller applications or startups with limited budgets.
  • Limited Ecosystem
    While growing, Spanner's ecosystem is not as mature as more established relational or NoSQL databases, which might lead to fewer third-party tools and integrations.
  • Complexity in Migration
    Migrating existing applications and data to Cloud Spanner can be complex and time-consuming, particularly for those coming from non-relational database systems.
  • Limited NoSQL Features
    For applications that require specific NoSQL features, such as unstructured data handling and schema flexibility, Cloud Spanner may not be the best fit compared to other NoSQL databases.
  • Regional Lock-in
    Although it offers global distribution, data residency and compliance requirements might limit some organizations to specific regions, which can affect the strategic deployment of an application.

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.

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

Google Cloud Spanner videos

+ Add

Build with Google Cloud Spanner

Category Popularity

0-100% (relative to Apache Spark and Google Cloud Spanner)

Apache Spark

Google Cloud Spanner

Databases
76 76%
Databases
24% 24
Big Data
100 100%
Big Data
0% 0
Relational Databases
0 0%
Relational Databases
100% 100
Stream Processing
100 100%
Stream Processing
0% 0

User comments

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Reviews

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

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

Google Cloud Spanner Reviews

We have no reviews of Google Cloud Spanner yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Google Cloud Spanner. It has been mentiond 72 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 (72)

  • Gravitino - the unified metadata lake
    In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / about 2 months ago
  • Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
    Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / 3 months ago
  • 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 / 5 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 / 6 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 / 7 months ago
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Google Cloud Spanner mentions (17)

  • Golden Ticket To Explore Google Cloud
    Multiregion is possible in Google Cloud using Cloud Spanner, which allows you to replicate the database not only in multiple zones but also in multiple regions as defined in the instance configuration. The replicas allow you to read data with low latency from multiple locations that are close to or within the region in the configuration. - Source: dev.to / about 2 years ago
  • /u/ryuuthecat wonders how a feature of google maps works. Engineer who programmed the feature responds with the answer
    Basically everything I touch is in-house, but a majority of it is available publicly. For instance: https://cloud.google.com/spanner/. Source: almost 3 years ago
  • How Do Companies (Like Evernote) Handle So Many Notes?
    An application that needs to handle a lot of data can use a distributed database like Cloud Spanner. Unlimited scale and you don't have to split your database into multiple tables. Source: almost 3 years ago
  • One of my favorite topics in DE is CAP Theorem. Has anyone managed to accomplish all 3 at once yet or is it truly impossible like the theorem states.
    Look at the architecture and performance of Google's Cloud Spanner, a CP system with 99.999% availability... https://cloud.google.com/spanner. Source: almost 3 years ago
  • Vaultree and AlloyDB: the world's first Fully Homomorphic and Searchable Cloud Encryption Solution
    In my opinion, Google has built some fantastic database services like Bigtable and Spanner, which literally changed the industry for good, and I am eager to see how they will build upon this new service. With AlloyDB's disaggregated architecture, the dystopian world where I only pay for SQL databases per query and the stored data on GCP seems closer than ever. - Source: dev.to / almost 3 years ago
View more

What are some alternatives?

When comparing Apache Spark and Google Cloud Spanner, 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.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

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

Oracle DBaaS - See how Oracle Database 12c enables businesses to plug into the cloud and power the real-time enterprise.

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

MySQL - The world's most popular open source database

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