Everything You Need to Know About Gmail RenderingLitmus
Gmail is a headache for people who make and send email.
Until September 2016, Gmail required the use of inlined CSS and didn’t support responsive email. A major update to the Gmail rendering engine rolled out support for embedded styles and media queries. Months after the update, rendering fragmentation still remains and is cause for confusion in the email community.
Everything You Need to Know About Gmail RenderingLitmus
Gmail is a headache for people who make and send email.
Until September 2016, Gmail required the use of inlined CSS and didn’t support responsive email. A major update to the Gmail rendering engine rolled out support for embedded styles and media queries. Months after the update, rendering fragmentation still remains and is cause for confusion in the email community.
Modern Oracle DBAs have spent years acquiring extremely valuable skills, even while facing increased responsibility for growing numbers of diverse multi-version databases, demands to transition to public cloud computing Infrastructure, and a never-ending drumbeat for upskilling and relevance in our industry. It’s the perfect time to consider a transition in your career by leveraging your expertise with the Oracle database in a new role as a Data Engineer (DE).
Hekaton is the original project name for In-Memory OLTP and just sounds cooler for a title name. Keeping up the tradition of deep technical “Inside” sessions at PASS, this half-day talk will take you behind the scenes and under the covers on how the In-Memory OLTP functionality works with SQL Server.
We will cover “everything Hekaton”, including how it is integrated with the SQL Server Engine Architecture. We will explore how data is stored in memory and on disk, how I/O works, how native complied procedures are built and executed. We will also look at how Hekaton integrates with the rest of the engine, including Backup, Restore, Recovery, High-Availability, Transaction Logging, and Troubleshooting.
Demos are a must for a half-day session like this and what would an inside session be if we didn’t bring out the Windows Debugger. As with previous “Inside…” talks I’ve presented at PASS, this session is level 500 and not for the faint of heart. So read through the docs on In-Memory OLTP and bring some extra pain reliever as we move fast and go deep.
This session will appear as two sessions in the program guide but is not a Part I and II. It is one complete session with a small break so you should plan to attend it all to get the maximum benefit.
From usability to performance, analytics to architecture; as report developers, the user experience design (UX) of your data model is quickly becoming more important than the pretty pictures that sit on top of it. This session will concentrate on the design decisions needed to increase the usage of your reports.
Social media analytics using Azure TechnologiesKoray Kocabas
Social media are computer-mediated tools that allow people to create, share or exchange information, ideas, and pictures/videos in virtual communities and networks. To sum up Social Media is everything for your customers and Your company need to listen them to understand, make a custom offer or improve loyalty etc. Azure Stream Analytics and HDInsight platforms can solve this problem for you. We'll focus on how to get Twitter data using Stream Analytics and how to make data enrichment and storing using HDInsight and What is the problem about sentiment analytics using Azure Machine Learning.
Let’s face it: Best Practices are too many to really know them all and choose which ones should be applied first. Does your telephone ring all the time? Do your users ask for that “quick report” that instead takes ages and keeps changing every time you think it’s done? Have you ever thought that in dire times avoiding Worst Practices could be a good starting point and you can leave fine tuning for a better future? If the answer is “yes”, then this session is for you: we will discover together how not to torture a SQL Server instance and we will see how to avoid making choices that in the long run could turn out to be not as smart as they looked initially.
Leveraging Open Source Automated Data Science ToolsDomino Data Lab
The data science process seeks to transform and empower organizations by finding and exploiting market inefficiencies and potentially hidden opportunities, but this is often an expensive, tedious process. However, many steps can be automated to provide a streamlined experience for data scientists. Eduardo Arino de la Rubia explores the tools being created by the open source community to free data scientists from tedium, enabling them to work on the high-value aspects of insight creation and impact validation.
The promise of the automated statistician is almost as old as statistics itself. From the creations of vast tables, which saved the labor of calculation, to modern tools which automatically mine datasets for correlations, there has been a considerable amount of advancement in this field. Eduardo compares and contrasts a number of open source tools, including TPOT and auto-sklearn for automated model generation and scikit-feature for feature generation and other aspects of the data science workflow, evaluates their results, and discusses their place in the modern data science workflow.
Along the way, Eduardo outlines the pitfalls of automated data science and applications of the “no free lunch” theorem and dives into alternate approaches, such as end-to-end deep learning, which seek to leverage massive-scale computing and architectures to handle automatic generation of features and advanced models.
When distributed system fail, they usually do so in spectacular ways that often have disastrous effects on your systems and users. This baptism by fire is commonly how we learn how big data systems really work. This presentation looks at real-world examples of failures using Java big data technologies such as Hadoop, Spark, Cassandra, or Kafka.
Data Exploration with Apache Drill: Day 1Charles Givre
Study after study shows that data scientists and analysts spend between 50% and 90% of their time preparing their data for analysis. Using Drill, you can dramatically reduce the time it takes to go from raw data to insight. This course will show you how.
The course material for this presentation are available at https://github.com/cgivre/data-exploration-with-apache-drill
CCM AlchemyAPI and Real-time AggregationVictor Anjos
An exploratory look into KairosDB (OpenTSDB) connected to Cassandra (CCM) and using AlchemyAPI for entity, topic and sentiment extraction.
Sprinkled in is a bit of Data Modeling, Truth Tables, Primary Keys, Partition Keys and Cluster Keys.
All written in Python!
23 October 2013 - AWS 201 - A Walk through the AWS Cloud: Introduction to Ama...Amazon Web Services
Amazon Redshift is the new data warehouse service from Amazon Web Services. Redshift offers you fast query performance when analyzing data sets from a few hundred gigabytes to over a petabyte at a fraction of the cost of traditional solutions. In this webinar, we will take a detailed look at Redshift, including a live demonstration. This webinar is ideal for anyone looking to gain deeper insight into their data, without the usual challenges of time, cost and effort.
Agile Data Science 2.0 covers the theory and practice of applying agile methods to the practice of applied analytics research called data science. The book takes the stance that data products are the preferred output format for data science teams to effect change in an organization. Accordingly, we show how to "get meta" to enable agility in building applications describing the applied research process itself. Then we show how to use 'big data' tools to iteratively build, deploy and refine analytics applications. Tracking data-product development through the five stages of the "data value pyramid", we show you how to build applications from conception through development through deployment and then through iterative improvement. Application development is a fundamental skill for a data scientist, and by publishing your data science work as a web application, we show you how to effect maximal change within your organization.
Technologies covered include Python, Apache Spark (Spark MLlib, Spark Streaming), Apache Kafka, MongoDB, ElasticSearch and Apache Airflow.
Modern Oracle DBAs have spent years acquiring extremely valuable skills, even while facing increased responsibility for growing numbers of diverse multi-version databases, demands to transition to public cloud computing Infrastructure, and a never-ending drumbeat for upskilling and relevance in our industry. It’s the perfect time to consider a transition in your career by leveraging your expertise with the Oracle database in a new role as a Data Engineer (DE).
Hekaton is the original project name for In-Memory OLTP and just sounds cooler for a title name. Keeping up the tradition of deep technical “Inside” sessions at PASS, this half-day talk will take you behind the scenes and under the covers on how the In-Memory OLTP functionality works with SQL Server.
We will cover “everything Hekaton”, including how it is integrated with the SQL Server Engine Architecture. We will explore how data is stored in memory and on disk, how I/O works, how native complied procedures are built and executed. We will also look at how Hekaton integrates with the rest of the engine, including Backup, Restore, Recovery, High-Availability, Transaction Logging, and Troubleshooting.
Demos are a must for a half-day session like this and what would an inside session be if we didn’t bring out the Windows Debugger. As with previous “Inside…” talks I’ve presented at PASS, this session is level 500 and not for the faint of heart. So read through the docs on In-Memory OLTP and bring some extra pain reliever as we move fast and go deep.
This session will appear as two sessions in the program guide but is not a Part I and II. It is one complete session with a small break so you should plan to attend it all to get the maximum benefit.
From usability to performance, analytics to architecture; as report developers, the user experience design (UX) of your data model is quickly becoming more important than the pretty pictures that sit on top of it. This session will concentrate on the design decisions needed to increase the usage of your reports.
Social media analytics using Azure TechnologiesKoray Kocabas
Social media are computer-mediated tools that allow people to create, share or exchange information, ideas, and pictures/videos in virtual communities and networks. To sum up Social Media is everything for your customers and Your company need to listen them to understand, make a custom offer or improve loyalty etc. Azure Stream Analytics and HDInsight platforms can solve this problem for you. We'll focus on how to get Twitter data using Stream Analytics and how to make data enrichment and storing using HDInsight and What is the problem about sentiment analytics using Azure Machine Learning.
Let’s face it: Best Practices are too many to really know them all and choose which ones should be applied first. Does your telephone ring all the time? Do your users ask for that “quick report” that instead takes ages and keeps changing every time you think it’s done? Have you ever thought that in dire times avoiding Worst Practices could be a good starting point and you can leave fine tuning for a better future? If the answer is “yes”, then this session is for you: we will discover together how not to torture a SQL Server instance and we will see how to avoid making choices that in the long run could turn out to be not as smart as they looked initially.
Leveraging Open Source Automated Data Science ToolsDomino Data Lab
The data science process seeks to transform and empower organizations by finding and exploiting market inefficiencies and potentially hidden opportunities, but this is often an expensive, tedious process. However, many steps can be automated to provide a streamlined experience for data scientists. Eduardo Arino de la Rubia explores the tools being created by the open source community to free data scientists from tedium, enabling them to work on the high-value aspects of insight creation and impact validation.
The promise of the automated statistician is almost as old as statistics itself. From the creations of vast tables, which saved the labor of calculation, to modern tools which automatically mine datasets for correlations, there has been a considerable amount of advancement in this field. Eduardo compares and contrasts a number of open source tools, including TPOT and auto-sklearn for automated model generation and scikit-feature for feature generation and other aspects of the data science workflow, evaluates their results, and discusses their place in the modern data science workflow.
Along the way, Eduardo outlines the pitfalls of automated data science and applications of the “no free lunch” theorem and dives into alternate approaches, such as end-to-end deep learning, which seek to leverage massive-scale computing and architectures to handle automatic generation of features and advanced models.
When distributed system fail, they usually do so in spectacular ways that often have disastrous effects on your systems and users. This baptism by fire is commonly how we learn how big data systems really work. This presentation looks at real-world examples of failures using Java big data technologies such as Hadoop, Spark, Cassandra, or Kafka.
Data Exploration with Apache Drill: Day 1Charles Givre
Study after study shows that data scientists and analysts spend between 50% and 90% of their time preparing their data for analysis. Using Drill, you can dramatically reduce the time it takes to go from raw data to insight. This course will show you how.
The course material for this presentation are available at https://github.com/cgivre/data-exploration-with-apache-drill
CCM AlchemyAPI and Real-time AggregationVictor Anjos
An exploratory look into KairosDB (OpenTSDB) connected to Cassandra (CCM) and using AlchemyAPI for entity, topic and sentiment extraction.
Sprinkled in is a bit of Data Modeling, Truth Tables, Primary Keys, Partition Keys and Cluster Keys.
All written in Python!
23 October 2013 - AWS 201 - A Walk through the AWS Cloud: Introduction to Ama...Amazon Web Services
Amazon Redshift is the new data warehouse service from Amazon Web Services. Redshift offers you fast query performance when analyzing data sets from a few hundred gigabytes to over a petabyte at a fraction of the cost of traditional solutions. In this webinar, we will take a detailed look at Redshift, including a live demonstration. This webinar is ideal for anyone looking to gain deeper insight into their data, without the usual challenges of time, cost and effort.
Agile Data Science 2.0 covers the theory and practice of applying agile methods to the practice of applied analytics research called data science. The book takes the stance that data products are the preferred output format for data science teams to effect change in an organization. Accordingly, we show how to "get meta" to enable agility in building applications describing the applied research process itself. Then we show how to use 'big data' tools to iteratively build, deploy and refine analytics applications. Tracking data-product development through the five stages of the "data value pyramid", we show you how to build applications from conception through development through deployment and then through iterative improvement. Application development is a fundamental skill for a data scientist, and by publishing your data science work as a web application, we show you how to effect maximal change within your organization.
Technologies covered include Python, Apache Spark (Spark MLlib, Spark Streaming), Apache Kafka, MongoDB, ElasticSearch and Apache Airflow.
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Tools and Tips: From Accidental to Efficient Data Warehouse Developer (24 Hours of PASS: Summit Preview)
1. Tools and Tips:
From Accidental to Efficient Data Warehouse Developer
Cathrine Wilhelmsen, Data Platform MVP
Moderated by: Christian Reich
2. Session Description
You have probably heard about the Accidental DBA, but what about the Accidental
Data Warehouse Developer? We stumbled into the world of data warehousing, learned
dimensional modeling and work with T-SQL and SSIS daily. We are masters of googling
solutions to our problems and making sure our ETL processes run without errors. We
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good thing! But how do we keep up with the increased demand?
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processes over night, but there are many things you can do to increase your own
productivity and become a more efficient and valuable Data Warehouse developer.
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tools for query analysis and tuning, free tools and scripts, Biml for SSIS and even a
couple of things I used to think were only useful for those scary DBAs.
50. SARGable Queries
"The query can efficiently seek using an index to find
the rows searched for in WHERE or JOIN clauses"
Compare it to finding a person in a phone book
(…let's just pretend we still use phone books…)
51. SARGable Queries
Adama, Lee
Adama, William
Agathon, Karl
Baltar, Gaius
Dualla, Anastasia
Gaeta, Felix
Henderson, Cally
Roslin, Laura
Thrace, Kara
Tigh, Saul
Tyrol, Galen
Valerii, Sharon
Find all rows where Name starts with 'T'
52. SARGable Queries
Adama, Lee
Adama, William
Agathon, Karl
Baltar, Gaius
Dualla, Anastasia
Gaeta, Felix
Henderson, Cally
Roslin, Laura
Thrace, Kara
Tigh, Saul
Tyrol, Galen
Valerii, Sharon
Find all rows where Name starts with 'T'
53. Non-SARGable Queries
"The query has to scan each row in the table to find the
rows searched for in WHERE or JOIN clauses"
Compare it to finding a person in a phone book
(…let's just keep pretending we still use phone books…)
54. Non-SARGable Queries
Adama, Lee
Adama, William
Agathon, Karl
Baltar, Gaius
Dualla, Anastasia
Gaeta, Felix
Henderson, Cally
Roslin, Laura
Thrace, Kara
Tigh, Saul
Tyrol, Galen
Valerii, Sharon
Find all rows where Name contains 'al'
55. Non-SARGable Queries
Adama, Lee
Adama, William
Agathon, Karl
Baltar, Gaius
Dualla, Anastasia
Gaeta, Felix
Henderson, Cally
Roslin, Laura
Thrace, Kara
Tigh, Saul
Tyrol, Galen
Valerii, Sharon
Find all rows where Name contains 'al'