Monty  Boehm

Monty Boehm

1660266240

SnpArrays.jl: Compressed Storage for SNP Data

SnpArrays.jl

Routines for reading and manipulating compressed storage of biallelic SNP (Single-Nucleotide Polymorphism) data.

Data from genome-wide association studies (GWAS) are often saved as a PLINK binary biallelic genotype table or .bed file. To be useful, such files should be accompanied by a .fam file, containing metadata on the rows of the table, and a .bim file, containing metadata on the columns. The .fam and .bim files are in tab-separated format.

Linear algebra operations on the PLINK formatted data now support multi-threading and GPU (CUDA) computing.

Installation

This package requires Julia v1.5 or later, which can be obtained from https://julialang.org/downloads/ or by building Julia from the sources in the JuliaLang/julia repository.

This package is registered in the default Julia package registry, and can be installed through standard package installation procedure.

using Pkg
pkg"add SnpArrays"

Use the backspace key to return to the Julia REPL.

Citation

If you use OpenMendel analysis packages in your research, please cite the following reference in the resulting publications:

Zhou H, Sinsheimer JS, Bates DM, Chu BB, German CA, Ji SS, Keys KL, Kim J, Ko S, Mosher GD, Papp JC, Sobel EM, Zhai J, Zhou JJ, Lange K. OPENMENDEL: a cooperative programming project for statistical genetics. Hum Genet. 2020 Jan;139(1):61-71. doi: 10.1007/s00439-019-02001-z. Epub 2019 Mar 26. PMID: 30915546; PMCID: PMC6763373.

Acknowledgments

Current implementation incorporates ideas in the package BEDFiles.jl by Doug Bates (@dmbates).

Chris Elrod (@chriselrod) helped us accelerate CPU linear algebra through his great support of LoopVectorization.jl package.

This project has been supported by the National Institutes of Health under awards R01GM053275, R01HG006139, R25GM103774, and 1R25HG011845.

Download Details:

Author: OpenMendel
Source Code: https://github.com/OpenMendel/SnpArrays.jl 
License: View license

#julia #storage #data 

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SnpArrays.jl: Compressed Storage for SNP Data
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Sid  Schuppe

Sid Schuppe

1618404240

Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses

Gerhard  Brink

Gerhard Brink

1622603640

The Best Options to Store Data and Keep it Safe Forever

In a data-driven age, such as the one we live in today, keeping our files is more or less preserving our history. Both the personal and emotional one and the professional one. All of us produce hundreds of megabytes of data every day, including photos and videos that we make with smartphones, the files we share with friends and those we work on in the office.

Yet, strangely, hardly any of us are wondering how to save data. Archiving is something very few think about and even fewer do regularly. So maybe we took thousands of photos of our children or our dog, but when we look for them, we never find them.

And, sometimes, we just don’t find them anymore. But what is the most effective, economical and long-lasting method for storing our data?

#cloud-storage #data-storage #storage #backup #options-to-store-data #store-data #data #hard-drive

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt