HIT Consultant Insightful coverage of healthcare innovation
 

extract transform load

Results 1 - 11 of 11Sort Results By: Published Date | Title | Company Name
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
Tags : 
    
Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Tags : 
    
Amazon Web Services
Published By: Attunity     Published Date: Feb 12, 2019
How can enterprises overcome the issues that come with traditional data warehousing? Despite the business value that data warehouses can deliver, too often they fall short of expectations. They take too long to deliver, cost too much to build and maintain, and cannot keep pace with changing business requirements. If this all rings a bell, check out Attunity’s knowledge brief on data warehouse automation with Attunity Compose. The solution automates the design, build, and deployment of data warehouses, data marts and data hubs, enabling more agile and responsive operation. The automation reduces time-consuming manual coding, and error-prone repetitive tasks. Read the knowledge brief to learn more about your options.
Tags : 
dwa, data warehouse automation, etl development, extract transform load tools, etl tools, data warehouse, data marts, data hubs data warehouse lifecycle, data integration, change management, data migration, consolidating data, cloud data warehousing, data warehouse design, attunity compose
    
Attunity
Published By: AWS     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Tags : 
    
AWS
Published By: DataFlux     Published Date: Jan 07, 2011
This white paper explores the various methods of data integration that can help an organization find a solution that fits its specific business needs.
Tags : 
dataflux, data integration, etl, extract, transform and load, enterprise, consolidation, software-as-a-service
    
DataFlux
Published By: IBM     Published Date: Feb 02, 2009
The implementation of an information strategy is critical to your organization's business success and key to sustaining your competitive advantage. IBM’s Information Agenda has been specifically designed to help. Philip Howard of Bloor Research has helpful insights on getting started.
Tags : 
ibm, information strategy, competitive advantage, ibm information agenda, master data management, mdm, etl, extract, transform and load, lifecycle management, data completeness, information agenda, information accelerators
    
IBM
Published By: SAP     Published Date: Jun 24, 2009
As information flows more freely in the business world, decisions need to be made quicker and based on sturdier data.
Tags : 
business intelligence, smb, sap, small business, medium business, best-in-class, time-to-information, laggards, benchmarking, knowledge management, performance management, technology, erp, crm, maturity, dashboard, maturity, pace, roi, return on investment
    
SAP
Published By: SAP     Published Date: Feb 21, 2008
Many significant business initiatives and large IT projects depend upon a successful data migration. Your goal is to minimize as much risk as possible through effective planning and scoping. This paper will provide insight into what issues are unique to data migration projects and offer advice on how to best approach them.
Tags : 
sap, data architect, data migration, business objects, information management software, bloor, sap r/3, application, enterprise applications, data quality management, master data management, mdm, extraction, transformation load, etl
    
SAP
Published By: SRC,LLC     Published Date: Jun 01, 2009
To mine raw data and extract crucial insights, business decision‐makers need fast and comprehensive access to all the information stored across their enterprise, regardless of its format or location. Furthermore, that data must be organized, analyzed and visualized in ways that permit easy interpretation of market opportunities growth, shifts and trends and the business‐process changes required to address them. Gaining a true perspective on an organization’s customer base, market area or potential expansion can be a challenging task, because companies use so many relational databases, data warehouse technologies, mapping systems and ad hoc data repositories to gather and house information for a wide variety of specialized purposes.
Tags : 
src, enterprise, enterprise applications, convergence, compared, counted, combined, reorganized, analyzed, visualized, mapped, database, gis, geographic business intelligence, data independence, etl, csv, delimited text file, mdb (both for microsoft access, esri personal geodatabase
    
SRC,LLC
Published By: SRC,LLC     Published Date: Jun 01, 2009
Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. Learn more today!
Tags : 
src, data transformation, classification, business value, geographic business intelligence, geo-bi, etl, extract, transform, load, spatial analysis, operational data stores, ods, spatial data, etl, cdi, point of sale, retail floor space management, rfsm, alteryx
    
SRC,LLC
Published By: Tidal Software     Published Date: Sep 03, 2008
Businesses can gain greater value from their BI investment by improving the way in which data flows to the BI system are managed. Many problems result when the ETL process is handled by a patchwork of scripting, custom coding, and various built-in schedulers that are part of existing ETL solutions, because these systems do not provide end-to-end execution, monitoring, and control of the ETL process.
Tags : 
business intelligence, data warehousing, data management, data integration, etl, extract–transform-load, bi operations, bi management, informatica, cognos, ibm datastage, sas, business objects, crystal enterprise, tidal, tidal software
    
Tidal Software
Search      

Add Research

Get your company's research in the hands of targeted business professionals.