Big Data & Machine Learning Solutions Decision Tree

Big Data Solutions Decision Tree

 Process of solution selection for Big Data projects is very complex with a lot of factors. Here is the decision tree, which maps the three types of problems to specific solutions. 

Machine Learning Solutions Decision Tree

Machine learning is a technique of data science that helps computers learn from existing data in order to forecast future behaviors, outcomes, and trends. Currently there are lot of products which can be used for this on-premises or in the cloud, based on single node or multiple nodes, in relational database or in Hadoop based storage.

Any data science project should be driven by business problems that means data science serves an organization by providing answers for its business problems and strategies in decision making process.
The chart below is a mapping from business problems into types of learning methods but it’s not a mapping from a specific business application to a specific scientific method. The right methods should be chosen according to a specific business problem and the end performance matric.

https://www.linkedin.com/pulse/data-science-landscape-ling-zhang

KDnuggets Data Science Software Poll Big Data vs Deep Learning affinity for top tools

http://bbs.pinggu.org/thread-4678414-1-1.html

Advanced Analytics & Business Intelligence Comparison Table

We know that analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of data to gain insight and drive business planning. Analytics consists of two major areas: Business Intelligence and Advanced Analytics.

http://newscentral.exsees.com/item/53349ecf406c333c9e3aa977a47166d8-28d29ae28711ca128d5e6fc7395808a6

Platform as a Service

Platform as a Service (PaaS):是一種雲端運算服務，提供運算平台與解決方案堆疊即服務。在雲端運算的典型層級中，平台即服務層介於軟體即服務與基礎設施即服務之間。
平台即服務提供使用者能將雲端基礎設施部署與建立至用戶端，或者藉此獲得使用程式語言、程式庫與服務。使用者不需要管理與控制雲端基礎設施，包含網路、伺服器、作業系統或儲存，但需要控制上層的應用程式部署與應用代管的環境。
Spark：Spark 的程式設計模型類似 MapReduce，但 Runtime 運行在記憶體中。Spark 能夠有效的利用記憶體做 Cache，所以特性非常上適合做迭代 (Iteration) 計算，例如統計迴歸、分群分類、協同過濾、降維等演算法。 
Spark Streaming：利用 Spark in-memory 特性，將串流的資料切分成很小的 Micro Batch 再交由 Spark 處理。 
Impala：採用 Massive Parallel Processing (MPP) 架構，提供針對 HDFS 上檔案的 SQL 查詢功能。

Middeware2012 crowd from mjfrankli

CrowdDB: Answering Queries with Crowdsourcing from Viviana Murello