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Big Data

Big data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.

Sample Architecture


Cinque Terre

Azure Big Data
Typical Azure Big Data Architecture includes Azure API Management, Backend Services, Data sources, Apps Sensors and devices, Event Hubs, Machine Learning, HDInsight (Apache Spark), Storage Power, BIStream Analytics, SQL Data Warehouse, Azure Data Factory & Azure Data Catalog.
Azure Data Lake Analytics A new distributed analytics service Distributed analytics service built on Apache YARN Elastic scale per query lets users focus on business goals-not configuring hardware Includes U-SQL-a language that unifies the benefits of SQL with the expressive power of C# Integrates with Visual Studio to develop, debug, and tune code faster Federated query across Azure data sources Enterprise-grade role based access control.
Azure Data Lake Store A Hyper-Scale Repository for Big Data Analytics Workloads Hadoop File System (HDFS) for the cloud No limits to scale Store any data in its native format Enterprise-grade access control, encryption at rest Optimized for analytic workload performance.

AWS Big Data
Amazon Web Services provides a broad and fully integrated portfolio of cloud computing services to help you build, secure, and deploy your big data applications. With AWS, there's no hardware to procure, and no infrastructure to maintain and scale, so you can focus your resources on uncovering new insights. With new capabilities and features added constantly, you'll always be able to leverage the latest technologies without making long-term investment commitments.
Data Movement: Import your data from on-premises, and in real-time.
Data Lake: Store any type of data securely, from gigabytes to exabytes.
Analytics: Analyze your data with a broad selection of analytic tools and engines.
Machine Learning: Forecast future outcomes, and prescribe action

Google BigQuery
BigQuery is Google's serverless, highly scalable, enterprise data warehouse designed to make all your data analysts productive at an unmatched price-performance. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL without the need for a database administrator. Analyze all your data by creating a logical data warehouse over managed, columnar storage, as well as data from object storage and spreadsheets. Build and operationalize machine learning solutions with simple SQL. Easily and securely share insights within your organization and beyond as datasets, queries, spreadsheets, and reports. BigQuery allows organizations to capture and analyze data in real time using its powerful streaming ingestion capability so that your insights are always current, and it's free for up to 1 TB of data analyzed each month and 10 GB of data stored.

Google BigData
Google BigQuery, Google Cloud Datalab and Google Cloud Dataproc are changing how you analyze and use data. Customers say tools like BigQuery are "nearly magical" because of their performance. Queries that used to take hours or days now take minutes or seconds. The result: more insights and value, realized by more people in more companies.

Cloudera Enterprise Data Hub
Cloudera curates and extends an open-source core with countless unique innovations, enhanced, de-risked, and centralized to meet your business-critical enterprise requirements. With Cloudera Shared Data Experience (SDX), you can bring your choice of analytics to the data, with a unified catalog that bridges the most complex environments and ensures consistent security and granular governance. Load or stream all data into a platform that can tune your workloads and ease lifecycle management. And operate with the same reliable shared services, regardless of where you data lives, in your preferred mix of hybrid- and multi-cloud environments.

Oracle Big Data Analytics
Put your data to work. Run predictive analytics models. Use machine learning to explore your data. Build dashboards for business leaders. Watch your investment in big data pay off.
Oracle Analytics Cloud A single platform that empowers your entire organization to ask any question of any data, in any environment, on any device.
Oracle Big Data Spatial and Graph Handle the most challenging graph, spatial, and processing workloads on Apache Hadoop and NoSQL database technologies.
Oracle Data Science Cloud Oracle Data Science Cloud is a collaborative, open, and enterprise-grade platform that helps data science teams become more productive and effective.
Oracle R Advanced Analytics for Hadoop The R interface for manipulating data stored in HDFS, using both HIVE transparency capabilities and mapping HDFS as direct input.