data analytics

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By: Attunity     Published Date: Nov 15, 2018
With the opportunity to leverage new analytic systems for Big Data and Cloud, companies are looking for ways to deliver live SAP data to platforms such as Hadoop, Kafka, and the Cloud in real-time. However, making live production SAP data seamlessly available wherever needed across diverse platforms and hybrid environments often proves a challenge. Download this paper to learn how Attunity Replicate’s simple, real-time data replication and ingest solution can empower your team to meet fast-changing business requirements in an agile fashion. Our universal SAP data availability solution for analytics supports decisions to improve operations, optimize customer service, and enable companies to compete more effectively.
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     Attunity
By: Attunity     Published Date: Nov 15, 2018
Change data capture (CDC) technology can modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems. To realize these benefits, enterprises need to understand how this critical technology works, why it’s needed, and what their Fortune 500 peers have learned from their CDC implementations. This book serves as a practical guide for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC. Read this book to understand: ? The rise of data lake, streaming and cloud platforms ? How CDC works and enables these architectures ? Case studies of leading-edge enterprises ? Planning and implementation approaches
Tags : optimize customer service
     Attunity
By: Attunity     Published Date: Nov 15, 2018
IT departments today face serious data integration hurdles when adopting and managing a Hadoop-based data lake. Many lack the ETL and Hadoop coding skills required to replicate data across these large environments. In this whitepaper, learn how you can provide automated Data Lake pipelines that accelerate and streamline your data lake ingestion efforts, enabling IT to deliver more data, ready for agile analytics, to the business.
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     Attunity
By: Attunity     Published Date: Jan 14, 2019
This whitepaper explores how to automate your data lake pipeline to address common challenges including how to prevent data lakes from devolving into useless data swamps and how to deliver analytics-ready data via automation. Read Increase Data Lake ROI with Streaming Data Pipelines to learn about: • Common data lake origins and challenges including integrating diverse data from multiple data source platforms, including lakes on premises and in the cloud. • Delivering real-time integration, with change data capture (CDC) technology that integrates live transactions with the data lake. • Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assemble a historical data store. • Leveraging a scalable and autonomous streaming data pipeline to deliver analytics-ready data sets for better business insights. Read this Attunity whitepaper now to get ahead on your data lake strategy in 2019.
Tags : data lake, data pipeline, change data capture, data swamp, hybrid data integration, data ingestion, streaming data, real-time data, big data, hadoop, agile analytics, cloud data lake, cloud data warehouse, data lake ingestion, data ingestion
     Attunity
By: Attunity     Published Date: Feb 12, 2019
This technical whitepaper by Radiant Advisors covers key findings from their work with a network of Fortune 1000 companies and clients from various industries. It assesses the major trends and tips to gain access to and optimize data streaming for more valuable insights. Read this report to learn from real-world successes in modern data integration, and better understand how to maximize the use of streaming data. You will also learn about the value of populating a cloud data lake with streaming operational data, leveraging database replication, automation and other key modern data integration techniques. Download this whitepaper today for about the latest approaches on modern data integration and streaming data technologies.
Tags : streaming data, cloud data lakes, cloud data lake, data lake, cloud, data lakes, streaming data, change data capture, cloud computing, modern data integration, data integration, data analytics, cloud-based data lake, enterprise data, self-service data
     Attunity
By: Attunity     Published Date: Feb 12, 2019
Read this technical whitepaper to learn how data architects and DBAs can avoid the struggle of complex scripting for Kafka in modern data environments. You’ll also gain tips on how to avoid the time-consuming hassle of manually configuring data producers and data type conversions. Specifically, this paper will guide you on how to overcome these challenges by leveraging innovative technology such as Attunity Replicate. The solution can easily integrate source metadata and schema changes for automated configuration real-time data feeds and best practices.
Tags : data streaming, kafka, metadata integration, metadata, data streaming, apache kafka, data integration, data analytics, database transactions, streaming environments, real-time data replication, data configuration
     Attunity
By: Avanade DACH     Published Date: May 08, 2018
In this six-step guide, we aim to help you solve your data challenges to prepare for advanced analytics, cognitive computing, machine learning and the resulting benefits of AI. We’ll show you how to get your data house in order, scale beyond the proof of concept stage, and develop an agile approach to data management. By continually repeating the steps in this guide, you’ll sharpen your data and shape it into a truly transformational business asset. You’ll be able to overcome some of the most common business problems, and work toward making positive changes: • Improve customer satisfaction • Reduce equipment outages • Increase marketing campaign ROI • Minimize fraud loss • Improve employee retention • Increase accuracy for financial forecasts
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     Avanade  DACH
By: Avi Networks     Published Date: Mar 07, 2018
"Maximizing Operational Efficiency and Application Performance in VMware-Based Data Center Some of the most common challenges in VMware-based virtual data center environments include: - Lack of visibility into applications and end-user experience - Complex and error-prone operations - High capital and operational costs Review our solution brief to learn how the Avi Controller, the industry’s first solution that integrates application delivery with real-time analytics, is able to solve these challenges."
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     Avi Networks
By: Avi Networks     Published Date: May 14, 2018
Avi Vantage is the only solution that delivers built-in application analytics in addition to enterprise-grade load balancing and application security. With millions of data points collected in real time, the platform delivers network-DVR like capabilities with the ability to record and display application analytics over specific time intervals (last 15 minutes, hour, day, week etc.) or for individual transactions. These application insights including total round trip time for each transaction, application health scores, errors, end user statistics, and security insights (DDoS attacks, SSL vulnerabilities, ciphers etc.) simplify troubleshooting of applications.
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     Avi Networks
By: AWS     Published Date: Nov 02, 2017
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it’s readily available to be categorized, processed, analyzed, and consumed by diverse groups within an organization. Since data - structured and unstructured - can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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     AWS
By: AWS     Published Date: Dec 15, 2017
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time. AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance. In this webinar, you’ll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences. Join this webinar to learn: How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development. How AWS supports BI solutions f
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     AWS
By: AWS     Published Date: Apr 27, 2018
Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now. Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data can now be processed in memory, and more significantly, analyzed as it arrives, in real time. Millions to hundreds of millions of events (such as video streams or application alerts) can be collected and analyzed per hour to deliver insights that can be acted upon in an instant. From financial services to manufacturing, this rev
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     AWS
By: AWS     Published Date: May 18, 2018
We’ve become a world of instant information. We carry mobile devices that answer questions in seconds and we track our morning runs from screens on our wrists. News spreads immediately across our social feeds, and traffic alerts direct us away from road closures. As consumers, we have come to expect answers now, in real time. Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now. Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data
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     AWS
By: AWS     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated q
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     AWS
By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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     AWS
By: AWS     Published Date: Sep 04, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
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     AWS
By: AWS     Published Date: Sep 04, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technology
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     AWS
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
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     AWS
By: AWS     Published Date: Oct 26, 2018
Today’s organisations are tasked with analysing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organisations are finding that in order to deliver analytic insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store enormous amounts of data in a central location, so it’s readily available to be categorised, processed, analysed, and consumed by diverse groups within an organisation? Since data—structured and unstructured—can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
Tags : data, lake, amazon, web, services, aws
     AWS
By: AWS     Published Date: Nov 15, 2018
It isn’t always easy to keep pace with today’s high volume of data, especially when it’s coming at you from a diverse number of sources. Tracking these analytics can place a strain on IT, who must provide the requested information to C-suite and analysts. Unless this process can happen quickly, the insights grow stale. Download your complimentary ebook now to see how Matillion ETL for Amazon Redshift makes it easy for technical and business users alike to participate and own the entire data and analysis process. With Matillion ETL for Amazon Redshift, everyone from CTOs to marketing analysts can generate valuable business intelligence by automating data and analytics orchestrations.
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     AWS
By: AWS     Published Date: Nov 15, 2018
"Getting the right analytics, quickly and easily, is important to help grow your organization. But analytics isn’t just about collecting and exploring data. The truly important step resides in converting this data into actionable insights. Acquiring these insights requires some planning ahead. While ease of deployment, time-to-insight, and cost are all important, there are several more assessments you need to take before choosing the right solution. Learn the 8 must-have features to look for in data visualization. Download this white paper to learn how TIBCO® Spotfire® in AWS Marketplace assist in providing you advanced, cost-effective analytics."
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     AWS
By: AWS     Published Date: Jan 03, 2019
Managing your data can be a challenge, but establishing an analytics solution that every user can navigate, regardless of skillset, is where organizations often need help. TIBCO® Spotfire® features AI-driven data visualizations and dashboards, which helps enables each organizational role to discover and deliver valuable insights with ease. Riteway Sales and Marketing, which helps many Southeastern supermarkets execute strategies, leveraged the power of TIBCO Spotfire to better understand individual product performance throughout their stores, achieving exponentially faster time to insight than their previous solution allowed. Watch this on-demand webinar to learn how TIBCO Spotfire, when leveraged on Amazon Web Services cloud, can help you generate deep insights in minutes. You’ll learn: • How to generate relevant, actionable insights from any data, anywhere • Some of the best practices for leveraging AI-driven visual and predictive analytics solutions in the cloud • How to
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     AWS
By: AWS     Published Date: Nov 28, 2018
Financial institutions run on data: collecting it, analyzing it, delivering meaningful insights, and taking action in real time. As data volumes increase, organizations demand a scalable analytics platform that can meet the needs of data scientists and business users alike. However, managing an on-premises analytics environment for a large and diverse user base can become time-consuming, costly, and unwieldy. Tableau Server on Amazon Web Services (AWS) is helping major Financial Services organizations shift data visualization and analytics workloads to the cloud. The result is fewer hours spent on manual work and more time to ask deeper questions and launch new data analyses, with easily-scalable support for large numbers of users. In this webinar, you’ll hear how one major asset management company made the shift to cloud data visualization with Tableau Server on AWS. Discover lessons learned, best practices tailored to Financial Services organizations, and starting tactics for scalable analytics on the cloud.
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     AWS
By: AWS     Published Date: Dec 17, 2018
Watch this webinar to learn how Tr?v Insurance Solutions, an insurance agency licensed to sell on-demand property and casualty insurance products, adopted DgSecure on Amazon Web Services (AWS) to anonymize production data to help comply with GDPR and other data privacy regulations. The solution helps Tr?v meet privacy standards while enabling its analytics teams to use data to better serve its clients.
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     AWS
By: AWS     Published Date: Dec 17, 2018
Watch this webinar to learn best practices from Zaloni for creating flexible, responsive, and cost-effective data lakes for advanced analytics that leverage Amazon Web Services (AWS).
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     AWS
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