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Google BigQuery vs Oracle: What are the differences?

Introduction

In this article, we will explore and compare the key differences between Google BigQuery and Oracle for data processing and analysis. Both BigQuery and Oracle are powerful database management systems, but they have distinct features and capabilities that set them apart.

  1. Scalability: One major difference between Google BigQuery and Oracle is their scalability. BigQuery is built on Google's cloud infrastructure, which makes it highly scalable, allowing it to handle massive datasets and process large-scale queries efficiently. On the other hand, Oracle's scalability is limited by the hardware resources of the underlying infrastructure it is deployed on.

  2. Storage System: Another key difference lies in their storage systems. BigQuery uses a columnar storage engine that is optimized for analytical workloads, enabling faster query performance on vast quantities of data. In contrast, Oracle uses a row-based storage engine, which is more suitable for transactional workloads that involve frequent updates and inserts.

  3. Pricing Model: The pricing models of BigQuery and Oracle also differ significantly. BigQuery follows a pay-as-you-go pricing model, where users are billed based on the amount of data processed and the queries performed. Oracle, on the other hand, typically follows a traditional licensing model, where users pay for the licenses based on their usage requirements and the number of server instances deployed.

  4. Managed Service: Google BigQuery is a fully managed service, which means that Google handles the management of the underlying infrastructure, including backups, maintenance, and security. This relieves the users from the burden of infrastructure management and allows them to focus solely on data analysis. In contrast, Oracle provides both cloud-based and on-premises solutions, which give users more control over their infrastructure but also require them to handle the management tasks themselves.

  5. Data Integration and Ecosystem: Google BigQuery integrates seamlessly with other Google Cloud services, such as Google Cloud Storage, Google Analytics, and Google Sheets, allowing users to easily import, export, and analyze data from these sources. Oracle, on the other hand, has a broader ecosystem of products and services that can be integrated with its database, including Oracle Analytics, Oracle Data Integrator, and Oracle Data Visualization.

  6. Query Language: Lastly, the query languages used by BigQuery and Oracle are different. BigQuery uses a variant of SQL called Standard SQL, which is compatible with the ANSI SQL standard and offers advanced analytical functions. Oracle, on the other hand, uses its own dialect of SQL called Oracle SQL, which includes proprietary features and extensions specific to Oracle's database engine.

In summary, Google BigQuery and Oracle differ in terms of scalability, storage system, pricing model, managed service, data integration, and query language. Each of these differences makes them suitable for different use cases and environments.

Decisions about Google BigQuery and Oracle
Daniel Moya
Data Engineer at Dimensigon · | 4 upvotes · 419K views

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

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We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.

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In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

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Pros of Google BigQuery
Pros of Oracle
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Expensive
  • 5
    Hard to maintain
  • 4
    Maintainable
  • 4
    Hard to use
  • 3
    High complexity

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Cons of Google BigQuery
Cons of Oracle
  • 1
    You can't unit test changes in BQ data
  • 14
    Expensive

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What is Google BigQuery?

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

What is Oracle?

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

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What are some alternatives to Google BigQuery and Oracle?
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Snowflake
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
Google Analytics
Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.
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