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

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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

ZDNet

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.


Multi-omics single-cell data integration and regulatory inference with graph-linked embedding - Nature Biotechnology

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Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE . Different single-cell data modalities are integrated at atlas-scale by modeling regulatory interactions.


Talend + SQL + Datawarehousing - Beginner to Professional

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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

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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.


Integrate.io Achieves Google Cloud Ready - BigQuery Designation

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"Integrate.io 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 Integrate.io "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?

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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)

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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

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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.


ETL or ELT? The Big Data age calls for the right integration strategy - ET CIO

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By Vikram Labhe It is a truism at this point to talk of the centrality of data for organisations. According to IDC, the global datasphere will rise at a compound annual growth rate (CAGR) of 23% between 2020-2025, highlighting the importance of responding to the surge in storage demand. For businesses to leverage data insights and drive growth, they must coordinate the dependencies and execute the different tasks on their data journey in the desired order, all while ensuring minimal impact from potential errors. Whether an organisation favours extract, transform, load (ETL) or extract, load, transform (ELT) will depend on their specific needs. Orchestration is fundamental for modern data processes, but for many businesses a modern data stack makes specific orchestration tools redundant.