Google Analytics and Dynatrace – Why you need both

Dynatrace

In this post, I wanted to share how I use Google Analytics together with Dynatrace to give me a more complete picture of my customers, and their experience across our digital channels. Google Analytics. Almost all marketers will be familiar with Google Analytics. Dynatrace news.

Digital Business Analytics: Let’s get started!

Dynatrace

We introduced Digital Business Analytics in part one as a way for our customers to tie business metrics to application performance and user experience, delivering unified insights into how these metrics influence business milestones and KPIs. A sample Digital Business Analytics dashboard.

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Tightening the communication within BizDevOps with Adobe Analytics & Dynatrace

Dynatrace

For example, I’ll get asked “We are using Adobe Analytics/Omniture SiteCatalyst , would we retire this when we use Dynatrace?” The only exception to this would be if you have Adobe Analytics for the sole purpose of understanding geographically where your users are coming from.

Introducing Digital Business Analytics: AI-powered real-time answers for better business outcomes

Dynatrace

On the other side of the organization, application owners have hired teams of analysts to dig through web analytics tools to gain insights into the customer experience. Welcome to Dynatrace Digital Business Analytics. Digital Business Analytics in action. Dynatrace news.

Real-Time Digital Twins: A New Approach to Streaming Analytics

ScaleOut Software

Consider the typical, conventional streaming analytics pipeline available on popular cloud platforms: A conventional pipeline combines telemetry from all data sources into a single stream which is queried by the user’s streaming analytics application.

Procella: unifying serving and analytical data at YouTube

The Morning Paper

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Evaluated over four typical YouTube Analytics query patterns, here are the performance and memory consumption figures for Artus vs Capacitor , Google BigQuery’s columnar storage format.

Introducing AthenaX, Uber Engineering’s Open Source Streaming Analytics Platform

Uber Engineering

These insights range from in-the-moment traffic conditions that provide guidance on trip routes to the Estimated Time of Delivery (ETD) of an UberEATS … The post Introducing AthenaX, Uber Engineering’s Open Source Streaming Analytics Platform appeared first on Uber Engineering Blog. Uber facilitates seamless and more enjoyable user experiences by channeling data from a variety of real-time sources.

Probabilistic Data Structures for Web Analytics and Data Mining

Highly Scalable

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases.

Follower Clusters – 3 Major Use Cases for Syncing SQL & NoSQL Deployments

Scalegrid

Data Analytics. In most cases, the culprit turns out to be an analytics job that is accessing tons of data and ends up slowing down the entire system. Here are the two options we typically suggest: If the analytics job is running on the primary/master server, move it to a secondary/replica server. If the analytics job is already running on a secondary node, and the performance degradation is unacceptable, we recommend moving the jobs to a dedicated analytics cluster.

Why Telcos Need a Real-Time Analytics Strategy

VoltDB

Historically, telco analytics have been limited and difficult. Analytics and insights have always taken a back seat to the first two priorities – accurate data processing and billing. In the telco world, batch processing has been the de facto choice for data processing and billing, so any analytics strategy had to be based on a batch processing model. Does this affect our analytics strategy? There is no substitute for real-time analytics and action.

How Netflix uses Druid for Real-time Insights to Ensure a High-Quality Experience

The Netflix TechBlog

apache realtime druid metrics-and-analytics kafkaBy Ben Sykes Continue reading on Netflix TechBlog ».

Observations on the Importance of Cloud-based Analytics

All Things Distributed

Many of these innovations will have a significant analytics component or may even be completely driven by it. For example many of the Internet of Things innovations that we have seen come to life in the past years on AWS all have a significant analytics components to it.

Follower Clusters – 3 Major Use Cases for Syncing SQL & NoSQL Deployments

High Scalability

Here are a few critical ways in which it differs from replication: Analytics Cluster Appliction monitoring Cloning Clustering Database Database Development Database Reporting Database Testing DevOps Follower Cluster MongoDB Monitoring MySQL Postgres Production Data Replication admin analytics best platform cluster clusters database database replication database scalability databases deployment infrastructure mysql mysql cluster mysql load optimization postgresql replication sql testing

Spice up your Analytics: Amazon QuickSight Now Generally Available in N. Virginia, Oregon, and Ireland.

All Things Distributed

SPICE sits between the user interface and the data source and can rapidly ingest all or part of the data into its fast, in-memory, columnar-based data store that’s optimized for analytical queries.

ScaleGrid DBaaS Expands MySQL Hosting Services Through AWS Cloud

Scalegrid

The platform allows MySQL AWS administrators to automate their time-consuming database operations in the cloud and improve their performance with high availability, disaster recovery, polyglot persistence, and advanced monitoring and analytics. PALO ALTO, Calif.,

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Testing AI-based apps? Think like a human

TechBeacon Testing

App Dev & Testing, Testing, Artificial Intelligence (AI), Machine Learning, Predictive Analytics, Special Coverage: TSQA 2020 ConferenceYour testing of software that includes artificial intelligence (AI) components will be more sophisticated and robust if you just think in human terms.

Driving down the cost of Big-Data analytics - All Things Distributed

All Things Distributed

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. All Things Distributed.

PostgreSQL Trends: Most Popular Cloud Providers, Languages, VACUUM, Query Management Strategies & Deployment Types in Enterprise

Scalegrid

PostgreSQL popularity is skyrocketing in the enterprise space. As this open source database continues to pull new users from expensive commercial database management systems like Oracle, DB2 and SQL Server, organizations are adopting new approaches and evolving their own to maintain the exceptional performance of their SQL deployments. We recently attended the PostgresConf event in San Jose to hear from the most active PostgreSQL user base on their database management strategies.

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What Does APM Stand For?: A Newbie Guide

DZone

devops monitoring management analytics operations eum euem newbie what is apmDefining APM. Today's blog post is headed back to the basics.

Building a responsible data capture policy

Dynatrace

The addition of the Digital Business Analytics module to the Dynatrace Software Intelligence Platform gives you a new way to understand the impact that application errors, performance and user behavior have on your business. Dynatrace news.

2019 PostgreSQL Trends Report: Private vs. Public Cloud, Migrations, Database Combinations & Top Reasons Used

High Scalability

PostgreSQL is an open source object-relational database system that has soared in popularity over the past 30 years from its active, loyal, and growing community. For the 2nd year in a row, PostgreSQL has kept the title of #1 fastest growing database in the world according to the DBMS of the Year report by the experts at DB-Engines. So what makes PostgreSQL so special, and how is it being used today?

How to Monitor MySQL Deployments with Prometheus & Grafana at ScaleGrid

Scalegrid

Monitoring your MySQL database performance in real-time helps you immediately identify problems and other factors that could be causing issues now or in the future. It’s also a good way to determine which components of the database can be enhanced or optimized to increase your efficiency and performance. This is usually done through monitoring software and tools either built-in to the database management software or installed from third-party providers.

The Next Generation in Logistics Tracking with Real-Time Digital Twins

ScaleOut Software

Traditional platforms for streaming analytics don’t offer the combination of granular data tracking and real-time aggregate analysis that logistics applications in operational environments such as these require.

The Next Generation in Logistics Tracking with Real-Time Digital Twins

ScaleOut Software

Traditional platforms for streaming analytics don’t offer the combination of granular data tracking and real-time aggregate analysis that logistics applications such as these require. With the real-time digital twin model, the next generation of streaming analytics has arrived.

Components of Effective Software Monitoring: App Logs, Infrastructure Telemetry, Health-Check Reports

DZone

App-specific metrics are complemented by other analytics to give a broader picture of system state and performance. software development log management monitoring and performance grafana health checks monitoring and alerting application log analytics graylog telemetry

Challenges Testers Face for Testing SMAC

DZone

There is a lot of discussion going currently about SMAC – Social, Mobile, Analytics and the Cloud. It also requires some prediction based on the analytics of real product usage. cloud performance analytics mobile testing testers cloud testingAn Intro to SMAC. SMAC is a new platform with a lot of growth expected in the years to come. Software testing is not only about testing individual parts of the application but also testing the integrated product associated with it.

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Engineering SQL Support on Apache Pinot at Uber

Uber Engineering

Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform. .

Search Console Speed Report: everything you need to know

Dareboost

Let’s discover how to use the Search Console Speed Report and how to interpret the related … Continue reading Search Console Speed Report: everything you need to know → Performance analytics

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Uber’s Data Platform in 2019: Transforming Information to Intelligence

Uber Engineering

Architecture Uber Data Big Data Data Analytics Data Platform EoY Infrastructure Product Platform Uber Engineering

Real-Time Digital Twins Simplify Code in Streaming Applications

ScaleOut Software

Properties in the data objects for all data sources can be fed to real-time aggregate analysis (performed by the stream-processing platform) to immediately spot patterns of interest in the analytic results generated for each data source.

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Real-Time Digital Twins Simplify Code in Streaming Applications

ScaleOut Software

Properties in the data objects for all data sources can be fed to real-time aggregate analysis (performed by the stream-processing platform) to immediately spot patterns of interest in the analytic results generated for each data source.

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How SeLoger use Dareboost to monitor short-lived URLs

Dareboost

… Continue reading How SeLoger use Dareboost to monitor short-lived URLs → Performance analytics

Digital Twins and Real-Time Digital Twins: What’s the Difference?

ScaleOut Software

ScaleOut Software has extended the concept of digital twins beyond PLM for use in real-time streaming analytics within live, mission-critical systems. provide context for streaming analytics.

Data Mining Problems in Retail

Highly Scalable

most of them are structured as data scientist manuals focusing on algorithms and methodologies and assume that human decisions play a central role in transforming analytical findings into business actions. This framework will later be used to describe analytical problems in a more uniform way.

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In-Stream Big Data Processing

Highly Scalable

It is great that the existing technologies like Hive, Storm, and Impala enable us to crunch Big Data using both batch processing for complex analytics and machine learning, and real-time query processing for online analytics, and in-stream processing for continuous querying.

Digital Twins Enable Seamless Use of Edge Computing in IoT

ScaleOut Software

In this application, it may make sense to migrate the low-level twin to the edge for better responsiveness and uninterrupted operations, while keeping the high-level, “strategic” twin in the cloud where computing resources are available to execute its predictive analytics algorithm.

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Scaling Uber’s Apache Hadoop Distributed File System for Growth

Uber Engineering

Architecture Apache Hadoop Big Data Big Data Analysis Garbage Collection Hadoop Hadoop Distributed File System Hadoop Infra & Analytics Team HBase HDFS HDFS Federation HIVE Hoodie Infrastructure JAR Jenkins NameNode PRC Puppet Router-based Federation Uber Uber Data Foundation Team Uber Engineering View File System WebHDFS YARNThree years ago, Uber Engineering adopted Hadoop as the storage ( HDFS ) and compute ( YARN ) infrastructure for our organization’s big data analysis.

Dareboost at Etam: when Web Performance meets a strong culture of innovation

Dareboost

Leslie] My Name is Leslie-Anne Buffignani, … Continue reading Dareboost at Etam: when Web Performance meets a strong culture of innovation → Performance analyticsWe often assist our clients in their web performance optimization projects.

Give Meaning to 100 Billion Events a Day — The Shift to Redshift

High Scalability

In part one , we described our Analytics data ingestion pipeline, with BigQuery sitting as our data warehouse. However, having our analytics events in BigQuery is not enough. Most importantly, data needs to be served to our end-users. TL;DR?—?Teads —?Teads

2019 Open Source Database Report: Top Databases, Public Cloud vs. On-Premise, Polyglot Persistence

Scalegrid

Ready to transition from a commercial database to open source, and want to know which databases are most popular in 2019? Wondering whether an on-premise vs. public cloud vs. hybrid cloud infrastructure is best for your database strategy? Or, considering adding a new database to your application and want to see which combinations are most popular?

Should You Use ClickHouse as a Main Operational Database?

Percona

What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. This information can be a mix of analytical (OLAP) queries (i.e. Analytical databases are optimized for a low number of slow queries. The most important limitations of the analytical databases are: Deletes and updates are non-existent or slow.

How to Estimate Web Performance Impact Before Making Fixes

José M. Pérez

It’s important to track these conversion events, and you can do it in your own system or leveraging tools like Google Analytics or Facebook Analytics. Then, he translated the metrics into 3 categories (good, average, and poor) and logged a custom dimension on Google Analytics.

Introduction to Benchmarking in Julia

DZone

But now that I’ve released OmniSci.jl , and as a company one of our major selling points is accelerated analytics , I figured it was time to stop assuming I wrote decent-ish code and pay attention to performance.