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Top Alternatives To Google BigQuery Data Scientist Should Know

Top Alternatives To Google BigQuery Data Scientist Should Know

Ambika Choudhury
W3Schools

Google BigQuery is a popular serverless, highly scalable multi-cloud data warehousing platform that ensures the successful storage of data collected from different sources. The key features of this platform include BigQuery Omni, Data QnA, BigQuery ML, BigQuery BI Engine, connected sheets and more.

Here we list the top 6 alternatives to Google BigQuery a Data Scientist should know.

(The list is in alphabetical order)



1| Amazon RedShift

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it simple and cost-effective to analyse all your data using standard SQL and your existing Business Intelligence (BI) tools. The tool allows you to run complex analytic queries against terabytes to petabytes of structured data, using sophisticated query optimisation, columnar storage on high-performance storage, and massively parallel query execution.

Some of the features are-

  • Redshift offers fast and flexible industry-leading performance.
  • It delivers query performance on datasets in a faster manner.
  • In this platform, the advanced machine learning capabilities deliver high throughput and performance.
  • The materialised views of this platform allow a user to achieve faster query performance for analytical workloads including dashboarding, queries from Business Intelligence (BI) tools as well as Extract, Load, Transform (ELT) data processing tasks. 
  • Redshift applies result caching to deliver sub-second response times for repeated queries.

Know more here.

2| Apache Hive

The Apache Hive is a popular data warehouse software, which facilitates reading, writing as well as managing large datasets that are residing in distributed storage and queried using SQL syntax. Built on top of Hadoop, Apache Hive provides a number of intuitive features.

Some of its features are-

  • The platform provides tools to enable easy access to data via SQL. This enables the data warehousing tasks such as extracting/transforming/loading (ETL), analysis of data, etc..
  • Apache Hive provides access to files stored either directly in Apache HDFS or in other data storage systems such as Apache HBase.
  • It provides a mechanism to impose structure on a variety of data formats.

Know more here.

3| Azure Synapse Analytics

Azure Synapse Analytics is an analytics service which combines enterprise data warehousing and Big Data analytics. Azure Synapse has four components, which are Synapse SQL, Spark, Synapse Pipelines and Studio. With this platform, data professionals can easily query relational as well as non-relational data at petabyte-scale by using the SQL language.   

Some of its features are-

  • Azure Synapse Analytics delivers insights from the data across data warehouses and other big data analytics systems.
  • The platform expands the discovery of insights from data and applies the machine learning models to intelligent apps.
  • Azure Synapse reduces project development time with a unified experience in order to develop end-to-end analytics solutions.

Know more here.

4| Google Cloud Bigtable

Google Cloud Bigtable is a high-performance data storage system built on Google File System. It is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Cloud Bigtable can be used to store as well as query various types of data including time-series data, marketing data, Internet of Things data and more.

Some of its features are-

See Also

  • This platform is ideal for collecting enormous amounts of data in a key-value store. 
  • It supports both read and write throughput at low latency in order to fast access to large amounts of data.
  • Throughput of Bigtable can be adjusted by adding or removing cluster nodes without the need for restarting it.

Know more here.

5| IBM Db2

IBM Db2 is a group of hybrid data management products that offer a complete suite of AI-empowered capabilities, The capabilities are designed to help you manage both structured and unstructured data on-premises as well as in private or public cloud environments

Some of its features are-

  • Designed for scalability and flexibility, IBM Db2 is built on an intelligent common SQL engine. 
  • This platform delivers predictive and proactive actionable insights into customer behaviour to help businesses grow market share, reduce costs and deliver successful AI initiatives.   

Know more here.

6| Snowflake

Snowflake is built on a multi-cluster and shared data architecture that is created for the cloud to revolutionise data warehousing, data lakes, including a host of other use cases. The platform features compute, storage, as well as cloud services layers that are logically integrated but scale independently from one another.

Some of its features are-

  • The multi-cluster shared data architecture of this platform is designed to process enormous quantities of data with maximum speed and efficiency.
  • Snowflake utilises micro-partitions to securely and efficiently store customer data. 
  • It eliminates the administration and management demands of traditional data platforms.
  • The platform outperforms traditional methods for executing data workloads.

Know more here

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