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Google BigQuery VS Apache Apex

Compare Google BigQuery VS Apache Apex and see what are their differences

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Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.

Apache Apex logo Apache Apex

Apache Apex is an enterprise-grade unified stream and batch processing engine.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • Apache Apex Landing page
    Landing page //
    2021-09-30

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

Apache Apex features and specs

No features have been listed yet.

Google BigQuery videos

+ Add

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

Apache Apex videos

No Apache Apex videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Google BigQuery and Apache Apex)

Google BigQuery

Apache Apex

Data Dashboard
97 97%
Data Dashboard
3% 3
Big Data
95 95%
Big Data
5% 5
Data Warehousing
94 94%
Data Warehousing
6% 6
Data Management
100 100%
Data Management
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 Google BigQuery and Apache Apex

Google BigQuery Reviews

Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQuery’s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or “heavy” queries that operate using a large set of data. This means it’s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io
16 Top Big Data Analytics Tools You Should Know About
Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. It also has built-in machine learning capabilities.
Source: www.analytixlabs.co.in

Apache Apex Reviews

We have no reviews of Apache Apex yet.
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Social recommendations and mentions

Based on our record, Google BigQuery seems to be a lot more popular than Apache Apex. While we know about 42 links to Google BigQuery, we've tracked only 1 mention of Apache Apex. 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.

Google BigQuery mentions (42)

  • Every Database Will Support Iceberg — Here's Why
    This isn’t hypothetical. It’s already happening. Snowflake supports reading and writing Iceberg. Databricks added Iceberg interoperability via Unity Catalog. Redshift and BigQuery are working toward it. - Source: dev.to / 19 days ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    Many of these companies first tried achieving real-time results with batch systems like Snowflake or BigQuery. But they quickly found that even five-minute batch intervals weren't fast enough for today's event-driven needs. They turn to RisingWave for its simplicity, low operational burden, and easy integration with their existing PostgreSQL-based infrastructure. - Source: dev.to / 24 days ago
  • How to Pitch Your Boss to Adopt Apache Iceberg?
    If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / about 1 month ago
  • Study Notes 2.2.7: Managing Schedules and Backfills with BigQuery in Kestra
    BigQuery Documentation: Google Cloud BigQuery. - Source: dev.to / 3 months ago
  • Docker vs. Kubernetes: Which Is Right for Your DevOps Pipeline?
    Pro Tip: Use Kubernetes operators to extend its functionality for specific cloud services like AWS RDS or GCP BigQuery. - Source: dev.to / 6 months ago
View more

Apache Apex mentions (1)

  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing Google BigQuery and Apache Apex, you can also consider the following products

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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

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