Collaborating Authors

information fusion

Learning Spark: Lightning-Fast Data Analytics: Damji, Jules S., Wenig, Brooke, Das, Tathagata, Lee, Denny: 9781492050049: Books


Most developers who grapple with big data are data engineers, data scientists, or machine learning engineers. This book is aimed at those professionals who are looking to use Spark to scale their applications to handle massive amounts of data. In particular, data engineers will learn how to use Spark's Structured APIs to perform complex data exploration and analysis on both batch and streaming data; use Spark SQL for interactive queries; use Spark's built-in and external data sources to read, refine, and write data in different file formats as part of their extract, transform, and load (ETL) tasks; and build reliable data lakes with Spark and the open source Delta Lake table format. For data scientists and machine learning engineers, Spark's MLlib library offers many common algorithms to build distributed machine learning models. We will cover how to build pipelines with MLlib, best practices for distributed machine learning, how to use Spark to scale single-node models, and how to manage and deploy these models using the open source library MLflow.

Big Data Exchange enters Indonesian data centre market with joint venture deal


Eileen Yu began covering the IT industry when Asynchronous Transfer Mode was still hip and e-commerce was the new buzzword. Currently an independent business technology journalist and content specialist based in Singapore, she has over 20 years of industry experience with various publications including ZDNet, IDG, and Singapore Press Holdings. Big Data Exchange (BDx) has marked its entry into Indonesia's data centre market through a joint venture agreement with PT Indosat and the latter's two subsidiaries. The move aims to tap increasing demand for cloud services and connectivity. Estimated to be worth $300 million, the deal would see BDx enter a conditional sale and purchase agreement of shares (CSPA) and establish a joint venture with PT Indosat, PT Aplikanusa Lintasarta, and PT Starone Mitra Telekomunikasi (SMT). Under the agreement, BDx, Indosat, and Lintasarta would set up data centre and cloud operations in the Asian market, BDx said in a statement Thursday.

Talend + SQL + Datawarehousing - Beginner to Professional


Talend is an Open Source/Enterprise ETL Tool, which can be used by Small to Large scale companies to perform Extract Transform and Load their data into Databases or any File Format (Talend supports almost all file formats and Database vendors available in the market including Cloud and other niche services). This Course is for anyone who wants to learn Talend from ZERO to HERO, it will also help in Enhancing your skills if you have prior experience with the tool. In the course we teach Talend - ETL tool, PostgreSQL - SQL and all the basic Datawarehousing concepts that you would need to work and excel in the organization or freelance. We give real world scenarios and try to explain the use of component so that it becomes more relevant and useful for your real world projects. By the end of the Course you will become the Master in Talend Data Intergration and will help you land the job as ETL or Talend Developer, which is high in demand.

Data Integration & ETL with Talend Open Studio Zero to Hero


Become a data savant and add value with ETL and your new knowledge! Talend Open Studio is an open, flexible data integration solution. But who actually lets them talk to each other? Become a data savant and add value with ETL and your new knowledge! Talend Open Studio is an open, flexible data integration solution. Achieves Google Cloud Ready - BigQuery Designation


" is thrilled to achieve BigQuery's designation! We look forward to continuing our ongoing partnership to drive the data stack evolution together and helping every organization to become data driven" Google Cloud Ready – BigQuery is a partner integration validation program that intends to increase customer confidence in partner integrations into BigQuery. As part of this initiative, Google engineering teams validate partner integrations into BigQuery in a three-phase process – Run a series of data integration tests, compare results against benchmarks, and work closely with partners to fill any gaps and refine documentation for our mutual customers. This designation enables customers to be confident that "Digital transformation increasingly requires analysis and access to data across multiple platforms and environments," said Manvinder Singh, Director, Partnerships at Google Cloud.

How can AI/ML improve sensor fusion performance?


Fusion at the data level simply fuses or aggregates multiple sensor data streams, producing a larger quantity of data, assuming that merging similar data sources results in increased precision and better information. Data level fusion is used to reduce noise and improve robustness. Fusion at the feature level uses features derived from several independent sensor nodes or a single node with several sensors. It combines those features into a multi-dimensional vector usable in pattern-recognition algorithms. Machine vision and localization functions are common applications of fusion at the feature level.

Multiblock Data Fusion in Statistics and Machine Learning - by Age K Smilde & Tormod Næs & Kristian Hovde Liland (Hardcover)


Arising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist. Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems. Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package.

Top 10 Essentials for Modern Data Integration - DATAVERSITY


Data integration challenges are becoming more difficult as the volume of data available to large organizations continues to increase. Business leaders clearly understand that their data is of critical value but the volume, velocity, and variety of data available today is daunting. Faced with these challenges, companies are looking for solutions with a scalable, high-performing data integration approach to support a modern data architecture. The problem is that just as data integration is increasingly complex, the number of potential solutions is endless. From DIY products built by an army of developers to out-of-the-box solutions covering one or more use cases, it's difficult to navigate the myriad of choices and subsequent decision tree.

AI Is The Main Ingredient In Adobe's Recipe For Post-Cookie Targeting And Personalization


Adobe is leaning on AI-powered data solutions to bridge the post-cookie identity gap. This fits into Adobe's broader strategy of using a mixture of automation and artificial intelligence to figure out what people are looking for and predict how brands can demonstrate value for customers in the moments that matter, said Kevin Lindsay, Adobe's director of product marketing. In practice, that means focusing on reducing churn and anticipating a customer's needs rather than just pushing to complete a transaction. Considering the rising cost of customer acquisition, convincing someone not to cancel a service can be more valuable than converting a new customer. "It's also about paying attention to signals and emotional cues, like frustration," Lindsay said, and [determining whether you're] ticking people off with a bad experience."

Reducing crime with better visualisation of data


Effective policing relies on good data. The prevention and reduction of crime, particularly serious and organised crime, depends on law enforcement agencies being able to gain swift insights from the huge and increasing amount of information at their disposal. The problem, given the sheer volume and variety of that data, is where to look first. So much of the data available to law enforcement data analysts and senior staff is unstructured. Police forces collect data of many different types – images from CCTV, phone records, social media conversations and images, and so on.