Before the database system was invented, data was commonly stored in flat files like text files or CSV files. OracleDB was invented very early with a very large market across the globe. Many of us must have used Oracle Database from the beginning but nowadays there are many competitors’ database products in the market. One of them is Google BigQuery which is creating a sensation around the tech industry. 

Google BigQquery provides a complete cloud data warehouse solution with data storage, querying data with fast processing while on the other hand Oracle is specialized in handling private and secured data for enterprise-level organizations.

Google BigQuery is a secure and scalable platform with built-in machine learning capabilities which display insights effortlessly for big data. Oracle uses the logical data structure to store data so that users can interact with the database seamlessly without knowing where the data is stored physically.

You will be learning about both Oracle BigQuery differences, their main features, and everything about them in detail. 

What is Google BigQuery?

Oracle BigQuery: Bigquery logo

Google BigQuery is a multi-cloud data warehouse that uses a build-in query engine and is highly scalable serverless, with a cost-effective computing model designed for business agility. It is owned by Google, launched on 19th May 2010. BigQuery is a fully-featured enterprise data warehouse that helps you manage and analyze your data. It uses the processing power of Google’s infrastructure to empower a single SQL query to analyze terabytes of data in seconds.

BigQuery is also called SQL-based Data Warehouse as a Service (DWaaS) that does not require any upfront hardware provisioning or management. It is a serverless warehouse for which SQL queries can be easily used to answer an organization’s biggest questions with zero infrastructure management.  BigQuery requires all requests to be authenticated. Google BigQuery has many built-in features like machine learning, geospatial analysis, and business intelligence. 

Google provides a complete package to their users with bulk data loading feature on Google Cloud Storage. This big data can easily be accessed using 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. 

Features of Google BigQuery

  • Serverless: BigQuery doesn’t require management of the infrastructure as it is serverless. Serverless BigQuery makes users focus on data and its analysis instead of resource management.
  • High-Performance: High Performance is the main reason for choosing Google BigQuery. BigQuery is a data warehouse with thousands of CPU cores and infinite storage, no maintenance, no provisioning, and can analyze petabyte-sized datasets in seconds. What more can a user need from the Data warehouse.
  • Integration: BigQuery has integrated with Google Cloud Partners for loading, transforming, and visualizing data. Data Integration from multiple sources to BigQuery makes the analytics job easier. BigQuery is very flexible and integrates with many tools like Google Analytics, Apache big data ecosystem, Cloud Data Fusion, Datastream, etc. easily. 
  • Availability: BigQuery provides high availability with highly durable and automatic replicated storage at multiple locations with no extra charge and no extra setup.
  • Real-time Analytics: Real-time data collection of BigQuery makes it possible to provide real-time and multi-cloud analytics solutions. BigQuery provides real-time integrated data, which makes a business’s latest data immediately available for analysis. 
  • Built-in ML and AI Capabilities: Google BIgQuery come with built-in Artificial Intelligence and Machine Learning model development and implementation capabilities. Machine Learning models can easily be created and executed using SQL queries.

What is Oracle?

Oracle BigQuery: oracle logo

Oracle is a Relational Database System(RDBMS) developed by Oracle Corporation on 16 June 1977.  Oracle is the most famous among all the relational databases, sometimes also called Oracle DB. Oracle is the RDBMS that implements object-oriented features like user-defined types, inheritance, and polymorphism, so it is also called an Object-Relational Database Management System (ORDBMS).

Oracle database was the first Database designed for data warehousing and enterprise grid computing. Enterprise grid computing makes oracle a flexible and cost-effective database to manage data. SQL queries are used to access data from Oracle. There are various databases editions of oracle are available which gives flexibility to oracle users to select editions according to their specific demands with a cost-effective solution.

Oracle keeps an eye on enterprise’s needs and keeps on updating technological developments. Oracle products are always updated with new features. Recently, the Oracle database is also available on Oracle Cloud. The next-generation oracle cloud is designed to run any application, faster and more securely, with less investment. 

Features of Oracle

  • Reliability: The main reason for choosing Oracle is its reliability. OracleDB provides the most secured and private database services to its clients. Oracle advanced security features have a mechanism for controlling and accessing the database to prevent unauthorized access. Oracle has some other security features like Oracle Database Vault and Oracle Label Security that regulate user privileges.
  • Availability: The OracleDB is never offline or out of service. It offers and maintains 24*7 availability of the database. OracleDB’s high availability is because of Oracle Data Guard functionality. Oracle DB is highly available because of its Real Application Cluster(RAC) mode. In RAC, one cluster node is a primary database and the second node is a secondary database which is a copy of the primary database. During any failure, data is available on another cluster node which makes it highly available and the system is always up and running.
  • Scalability and Performance: Oracle has features like Real Application Clustering and Portability which makes it highly scalable. Oracle is a multiuser database, and it provides top-notch performance with control data consistency and concurrency.
  • Portability: The Oracle database can be ported over 100 different hardware platforms and around 20 networking protocols. It is way more than any other competitor offers. By changing in platform and OS, it is easy to write Oracle applications securely.
  • Backup and Recovery: Oracle has features to recover data from any kind of failure. It is designed as RAC, as a result, all data and processes have backup and can be recovered in case of any failure scenario.
  • Analytics Solutions: OracleDB provides solutions for analytical calculations on business data by implementing OLAP (Oracle Analytic Processing) and Oracle Advanced Analytics.
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Oracle BigQuery Comparison: Key Differences

Let’s learn the key differences between Google BigQuery and Oracle: 

Base of ComparisonGoogle BigQueryOracle
Developed by and Initial ReleaseDeveloped by Google on 19th May 2010Developed by Oracle Corporation on 16 June 1977.
Database ModelRelational DBMSRelational DBMS
LicenseCommercialCommercial
Query LanguageSQLSQL
Supported Programming Language.Net, Java, JavaScript, Objective-C, PHP, Python, and RubyC, C#, C++, Java, JavaScript, Perl, PHP, Python, R, Ruby, Scala, Visual Basic, and many more
AI/ML IntegrationYesNo
Real-Time Data Collection and IntegrationYesNo
Multicloud CapabilitiesBigQuery OmniOracle Cloud Infrastructure(OCI)
BI Tool IntegrationYesYes, with Oracle Business Intelligence (BI)
ComplexityNoYes
Third-Party IntegrationsNoYes

Oracle BigQuery Key Differences in detail:

Oracle BigQuery Differences: Developed By and Initial Release

Google BigQuery was developed recently by Google on 19th May 2010 while oracle Database is a very old database developed by Oracle Corporation on 16 June 1977.

Oracle BigQuery Differences: Database Model

Both Google BigQuery and Oracle are based on a Relational Database management system(RDBMS). Although object-oriented features of BigQuery make it Object-Relational Database Management System (ORDBMS).

Oracle BigQuery Differences: License

Both are commercially available with different variants which can be bought by clients according to their needs and investment. Google keeps on updating BigQuery with new features at a very fast pace. Their latest release is in 2022 only.

Oracle’s latest version is 19C released in January 2019 for many platforms including the cloud. It is also full of features according to today’s demands. 

Oracle BigQuery Differences: Query Language

BigQuery supports SQL query language. It uses SQL queries for machine learning as well as for geospatial analysis.

Oracle also uses SQL query language to update and query data in tables.

Oracle BigQuery Differences: Supported Programming Language

Programming languages supported by BigQuery are .Net, Java, JavaScript, Objective-C, PHP, Python, and Ruby.

Oracle supports many programming languages like C, C#, C++, Java, JavaScript, Perl, PHP, Python, R, Ruby, Scala, Visual Basic, and others.

Oracle BigQuery Differences: AI/ML Integration

BigQuery provides integration with AI and Machine Learning features. It enables users to utilize Machine Learning capabilities like modeling and performing predictive analytics using SQL Queries.

Oracle does not support AI and Machine Learning features.

Oracle BigQuery Differences: Real-Time Data Collection and Integration

BigQuery supports real-time data collection from various sources and integration of data in the required format.

Oracle does not support real-time data collection and integration as of now.

Oracle BigQuery Differences: Multicloud Capabilities

Google provides Google Cloud Partners for BigQuery integration for loading, transforming, and visualizing data. Multi-cloud analytics solution for BigQuery is BigQuery Omni which is a flexible, fully manageable, cost-effective, and secured source to analyze data across clouds.

Oracle Cloud Infrastructure(OCI) is a set of complementary cloud services that offer high-performance compute capabilities and storage capacity with secured access. 

Oracle BigQuery Differences: BI Tool Integration

BigQuery provides cloud BI tool integration. It enables BigQuery to do data integration, transformation, analysis, visualization, and reporting seamlessly. 

Oracle launched Oracle Business Intelligence (BI) that provides an integrated, end-to-end Enterprise Performance Management System, including BI Tools.

Oracle BigQuery Differences: Complexity

Oracle is a bit complex to implement end-to-end database solutions as compared to Google BigQuery.

Oracle BigQuery Differences: Third-Party Integrations

Oracle supports third-party integrations while Google BigQuery does not.

Learn more about Loading data from oracle to BigQuery.

Conclusion

Google BigQuery has recently launched, a new generation cloud data warehouse solution provider. It is a fully-featured and end-to-end data solution for data loading, data integration, transformation, analysis, and visualizations. It supports most sensational current technologies like AI/ ML and geospatial analysis. 

While Oracle is a very early database with many features and a widely used database till now. Oracle keeps on updating and innovating features to provide support for current new technologies. In some new aspects, it is innovating well with Oracle Cloud Infrastructure(OCI) and Oracle Business Intelligence (BI) but does not provide support for AI/ML integrations. Although, Oracle is a very reliable and secured database.

It is very difficult to conclude which one is better,  the choice between is the two is completely user’s need-based.

However, as a Developer, extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, Marketing Platforms to your Oracle Database can seem to be quite challenging. If you are from non-technical background or are new in the game of data warehouse and analytics, Hevo Data can help!

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Senior Customer Experience Engineer

Veeresh specializes in JDBC, REST API, Linux, and Shell Scripting. He excels in resolving complex issues, conducting brainstorming sessions, and implementing Python transformations, contributing significantly to Hevo's success.

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