Características de Google Cloud BigQuery

Supports a variety of scripting environments

Mines data from databases and prepares data for analysis

Applies statistical algorithms to selected data

Analyzes both structured and unstructured data

Interacts with data to prepare it for visualizations and models

Offers modeling capabilities

Creates data visualizations or graphs

Generates reports of data performance

Unifies information on a singular platform

Helps marketers measure return on investment (ROI) by analyzing campaign effectiveness against costs

Gathers data about the effectiveness, impact, and reach of marketing campaigns

Collects and reports on data relating to customer journeys, preferences, and history

Allows multiple users access to a unified, transparent overview of analytics, dashboards, and campaign results

Includes features for budgeting, forecasting, and managing marketing investments

Offers a white labeling service for agencies or resellers to customize platform branding

Analyzes historical and current marketing campaigns to inform future strategy

Creates reports and dashboards to analyze results of campaigns

Identifies which marketing campaigns resolved in open or closed opportunities

Collects marketing campaign performance data across multiple channels

Provides opportunities for brands and businesses to fix or modify existing or future campaigns via feedback

Uses artificial intelligence (AI) to predict campaign outcomes and suggest actions for optimization

Collects, stores, and organizes massive, unstructured data in real time

Facilitates the disseminating of collected big data throughout parallel computing clusters

Creates a repository to collect and store raw data from sensors, devices, machines, files, etc.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Hadoop

Facilitates solution to run on and scale to a large number of machines and systems

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Aligns processing and distribution workflows on top of Apache Hadoop

Moves big data collection and processing to the cloud

Processes batch, real-time, and streaming data workloads in singular, multi-tenant, or cloud systems

Facilitates analysis of high-volume, real-time data.

Allows user to query data through query languages like SQL.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Spark

Integrates data from multiple external databases.

Facilitates the dissemination of collected big data throughout parallel computing clusters.

Processes data and represents interpretations in a variety of graphic formats.

Strings together specific functions and datasets to automate analytics iterations.

Isolates certain datasets and facilitates management of data access.

Allows big data tool to run and record data within external applications.

Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations

View ETL process data via reports and visualizations like charts and graphs.

Record ETL historical data for auditing and potential data correction needs.

Extract data from the designated source(s) like relational databases, JSON files, and XML files.

Cleanse and re-format extracted data to the needed target format.

Load reformatted data into target database, data warehouse, or other storage location.

Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly).

Capable of scaling processing power up or down based on ETL volume.

Consolidates, Cleanses and Normalizes data from multiple disparate sources.

Helps save storage capacity and improves query performance.

Eliminates data inconsistency and duplications ensuring data integrity.

SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.

Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.

Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS

Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.

Integrates with BI Tools to transform data into Actionable Insights.

Provides speed in data processing and capturing unstructured, semi-structured and streaming data.

Provides On-Premise deployment options.

Provides Cloud deployment options (private or public cloud, hybrid cloud).

Manages huge volumes of data, upscale or downscale as per demand.

Policies, procedures and standards to manage and access data.

Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion

Provides predefined system roles, privileges, and user-defined roles to users.

Allows integration with external security mechanisms like Kerberos, LDAP authentication etc.

Provides an audit log to track access and operations performed on databases for regulatory compliance.

Provides encryption capability for all the data at rest using encryption keys.

Stores data tables as columns.

Supports multiple data types like lists, sets, hashes (similar to map), sorted sets etc.

Implements auto horizontal data partitioning that allows storing data on more than one node to scale out.

Restores a database to a correct (consistent) state in the event of a failure.

Copy data across multiple servers through master-slave, peer-to-peer replication architecture etc.

Stores frequently-used data in system memory quickly.

Provides support to store, index and query data in more than one format.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Offers a centralize, neutral location for parties to conduct data analysis.

Offers localized computation, where data remains where it resides and is called by API in order to conduct analysis.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Works across multiple software systems or databases

Anticipates needs and offers suggestions without prompting