�ݺ�ߣshows by User: cgivre

�ݺ�ߣshows by User: cgivre / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: cgivre / Tue, 26 Mar 2019 02:17:13 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: cgivre Drilling Cyber Security Data With Apache Drill /slideshow/drilling-cyber-security-data-with-apache-drill/138177036 apachecon2019-drillingsecuitydata-190326021713
This deck walks you through using Apache Drill and Apache Superset (Incubating) to explore cyber security datasets including PCAP, HTTPD log files, Syslog and more.]]>
This deck walks you through using Apache Drill and Apache Superset (Incubating) to explore cyber security datasets including PCAP, HTTPD log files, Syslog and more.]]> Tue, 26 Mar 2019 02:17:13 GMT /slideshow/drilling-cyber-security-data-with-apache-drill/138177036 cgivre@slideshare.net(cgivre) Drilling Cyber Security Data With Apache Drill cgivre This deck walks you through using Apache Drill and Apache Superset (Incubating) to explore cyber security datasets including PCAP, HTTPD log files, Syslog and more. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachecon2019-drillingsecuitydata-190326021713-thumbnail.jpg?width=120&height=120&fit=bounds" /> This deck walks you through using Apache Drill and Apache Superset (Incubating) to explore cyber security datasets including PCAP, HTTPD log files, Syslog and more.

Drilling Cyber Security Data With Apache Drill from Charles Givre

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0 Blockchain and UDFs /slideshow/blockchain-and-udfs/80159236 blockchainudfs-170926033814
A brief presentation I gave at the Apache Drill hackathon in which I demonstrated plugins for Drill that enabled Drill to query Blockchain and XML as well as a few user defined functions.]]>
A brief presentation I gave at the Apache Drill hackathon in which I demonstrated plugins for Drill that enabled Drill to query Blockchain and XML as well as a few user defined functions.]]> Tue, 26 Sep 2017 03:38:14 GMT /slideshow/blockchain-and-udfs/80159236 cgivre@slideshare.net(cgivre) Blockchain and UDFs cgivre A brief presentation I gave at the Apache Drill hackathon in which I demonstrated plugins for Drill that enabled Drill to query Blockchain and XML as well as a few user defined functions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blockchainudfs-170926033814-thumbnail.jpg?width=120&height=120&fit=bounds" /> A brief presentation I gave at the Apache Drill hackathon in which I demonstrated plugins for Drill that enabled Drill to query Blockchain and XML as well as a few user defined functions.

Blockchain and UDFs from Charles Givre

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0 Data Exploration with Apache Drill: Day 2 /slideshow/data-exploration-with-apache-drill-day-2/72713653 dataexplorationwithapachedrill-day2-170301212305
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]]>
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]]> Wed, 01 Mar 2017 21:23:05 GMT /slideshow/data-exploration-with-apache-drill-day-2/72713653 cgivre@slideshare.net(cgivre) Data Exploration with Apache Drill: Day 2 cgivre 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 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataexplorationwithapachedrill-day2-170301212305-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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

Data Exploration with Apache Drill: Day 2 from Charles Givre

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0 Data Exploration with Apache Drill: Day 1 /slideshow/data-exploration-with-apache-drill-day-1/72713582 dataexplorationwithapachedrill-day1-170301212027
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]]>
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]]> Wed, 01 Mar 2017 21:20:27 GMT /slideshow/data-exploration-with-apache-drill-day-1/72713582 cgivre@slideshare.net(cgivre) Data Exploration with Apache Drill: Day 1 cgivre 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 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataexplorationwithapachedrill-day1-170301212027-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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

Data Exploration with Apache Drill: Day 1 from Charles Givre

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0 Killing ETL with Apache Drill /slideshow/killing-etl-with-apache-drill/69696305 apachedrillsmarttalk-161130201355
The Extract-Transform-Load (ETL) process is one of the most time consuming processes facing anyone who wishes to analyze data. Imagine if you could quickly, easily and scaleably merge and query data without having to spend hours in data prep. Well.. you don’t have to imagine it. You can with Apache Drill. In this hands-on, interactive presentation Mr. Givre will show you how to unleash the power of Apache Drill and explore your data without any kind of ETL process.]]>
The Extract-Transform-Load (ETL) process is one of the most time consuming processes facing anyone who wishes to analyze data. Imagine if you could quickly, easily and scaleably merge and query data without having to spend hours in data prep. Well.. you don’t have to imagine it. You can with Apache Drill. In this hands-on, interactive presentation Mr. Givre will show you how to unleash the power of Apache Drill and explore your data without any kind of ETL process.]]> Wed, 30 Nov 2016 20:13:55 GMT /slideshow/killing-etl-with-apache-drill/69696305 cgivre@slideshare.net(cgivre) Killing ETL with Apache Drill cgivre The Extract-Transform-Load (ETL) process is one of the most time consuming processes facing anyone who wishes to analyze data. Imagine if you could quickly, easily and scaleably merge and query data without having to spend hours in data prep. Well.. you don’t have to imagine it. You can with Apache Drill. In this hands-on, interactive presentation Mr. Givre will show you how to unleash the power of Apache Drill and explore your data without any kind of ETL process. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachedrillsmarttalk-161130201355-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Extract-Transform-Load (ETL) process is one of the most time consuming processes facing anyone who wishes to analyze data. Imagine if you could quickly, easily and scaleably merge and query data without having to spend hours in data prep. Well.. you don’t have to imagine it. You can with Apache Drill. In this hands-on, interactive presentation Mr. Givre will show you how to unleash the power of Apache Drill and explore your data without any kind of ETL process.

Killing ETL with Apache Drill from Charles Givre

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0 What Does Your Smart Car Know About You? Strata London 2016 /slideshow/what-does-your-smart-car-know-about-you-strata-london-2016/65822166 stratalondon2016-whatdoesyoursmartcarknowaboutyou-160908125301
In the last few years, auto makers and technology companies have introduced a variety of devices to connect cars to the Internet and use this connectivity to gather data about the vehicles’ activity, but these connected cars gather a considerable amount of data about their owners’ activities beyond what one might expect. In aggregate and combined with other datasets, this data represents a significant degradation of personal privacy as well as a potential security risk. As auto insurers and local governments start to require this data collection, consumers should be aware of the security risks as well as the potential privacy invasions associated with this unique type of data collection. In a follow-up to his 2015 session at Strata + Hadoop World NYC, Charles Givre examines data gathered from sensors in automobiles. Charles focuses on what kinds of data cars are gathering and asks critical questions about whether the benefits this data provides outweigh the risks and cost to personal privacy—the inevitable result of this data collection. ]]>
In the last few years, auto makers and technology companies have introduced a variety of devices to connect cars to the Internet and use this connectivity to gather data about the vehicles’ activity, but these connected cars gather a considerable amount of data about their owners’ activities beyond what one might expect. In aggregate and combined with other datasets, this data represents a significant degradation of personal privacy as well as a potential security risk. As auto insurers and local governments start to require this data collection, consumers should be aware of the security risks as well as the potential privacy invasions associated with this unique type of data collection. In a follow-up to his 2015 session at Strata + Hadoop World NYC, Charles Givre examines data gathered from sensors in automobiles. Charles focuses on what kinds of data cars are gathering and asks critical questions about whether the benefits this data provides outweigh the risks and cost to personal privacy—the inevitable result of this data collection. ]]> Thu, 08 Sep 2016 12:53:01 GMT /slideshow/what-does-your-smart-car-know-about-you-strata-london-2016/65822166 cgivre@slideshare.net(cgivre) What Does Your Smart Car Know About You? Strata London 2016 cgivre In the last few years, auto makers and technology companies have introduced a variety of devices to connect cars to the Internet and use this connectivity to gather data about the vehicles’ activity, but these connected cars gather a considerable amount of data about their owners’ activities beyond what one might expect. In aggregate and combined with other datasets, this data represents a significant degradation of personal privacy as well as a potential security risk. As auto insurers and local governments start to require this data collection, consumers should be aware of the security risks as well as the potential privacy invasions associated with this unique type of data collection. In a follow-up to his 2015 session at Strata + Hadoop World NYC, Charles Givre examines data gathered from sensors in automobiles. Charles focuses on what kinds of data cars are gathering and asks critical questions about whether the benefits this data provides outweigh the risks and cost to personal privacy—the inevitable result of this data collection. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratalondon2016-whatdoesyoursmartcarknowaboutyou-160908125301-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the last few years, auto makers and technology companies have introduced a variety of devices to connect cars to the Internet and use this connectivity to gather data about the vehicles’ activity, but these connected cars gather a considerable amount of data about their owners’ activities beyond what one might expect. In aggregate and combined with other datasets, this data represents a significant degradation of personal privacy as well as a potential security risk. As auto insurers and local governments start to require this data collection, consumers should be aware of the security risks as well as the potential privacy invasions associated with this unique type of data collection. In a follow-up to his 2015 session at Strata + Hadoop World NYC, Charles Givre examines data gathered from sensors in automobiles. Charles focuses on what kinds of data cars are gathering and asks critical questions about whether the benefits this data provides outweigh the risks and cost to personal privacy—the inevitable result of this data collection.

What Does Your Smart Car Know About You? Strata London 2016 from Charles Givre

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0 Apache Drill Workshop /slideshow/apache-drill-workshop/62779123 apachedrillworkshop-160606173002
Study after study show that data scientists spend 50-90 percent of their time gathering and preparing data. In many large organizations this problem is exacerbated by data being stored on a variety of systems, with different structures and architectures. Apache Drill is a relatively new tool which can help solve this difficult problem by allowing analysts and data scientists to query disparate datasets in-place using standard ANSI SQL without having to define complex schemata, or having to rebuild their entire data infrastructure. In this talk I will introduce the audience to Apache Drill—to include some hands-on exercises—and present a case study of how Drill can be used to query a variety of data sources. The presentation will cover: * How to explore and merge data sets in different formats * Using Drill to interact with other platforms such as Python and others * Exploring data stored on different machines]]>
Study after study show that data scientists spend 50-90 percent of their time gathering and preparing data. In many large organizations this problem is exacerbated by data being stored on a variety of systems, with different structures and architectures. Apache Drill is a relatively new tool which can help solve this difficult problem by allowing analysts and data scientists to query disparate datasets in-place using standard ANSI SQL without having to define complex schemata, or having to rebuild their entire data infrastructure. In this talk I will introduce the audience to Apache Drill—to include some hands-on exercises—and present a case study of how Drill can be used to query a variety of data sources. The presentation will cover: * How to explore and merge data sets in different formats * Using Drill to interact with other platforms such as Python and others * Exploring data stored on different machines]]> Mon, 06 Jun 2016 17:30:02 GMT /slideshow/apache-drill-workshop/62779123 cgivre@slideshare.net(cgivre) Apache Drill Workshop cgivre Study after study show that data scientists spend 50-90 percent of their time gathering and preparing data. In many large organizations this problem is exacerbated by data being stored on a variety of systems, with different structures and architectures. Apache Drill is a relatively new tool which can help solve this difficult problem by allowing analysts and data scientists to query disparate datasets in-place using standard ANSI SQL without having to define complex schemata, or having to rebuild their entire data infrastructure. In this talk I will introduce the audience to Apache Drill—to include some hands-on exercises—and present a case study of how Drill can be used to query a variety of data sources. The presentation will cover: * How to explore and merge data sets in different formats * Using Drill to interact with other platforms such as Python and others * Exploring data stored on different machines <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachedrillworkshop-160606173002-thumbnail.jpg?width=120&height=120&fit=bounds" /> Study after study show that data scientists spend 50-90 percent of their time gathering and preparing data. In many large organizations this problem is exacerbated by data being stored on a variety of systems, with different structures and architectures. Apache Drill is a relatively new tool which can help solve this difficult problem by allowing analysts and data scientists to query disparate datasets in-place using standard ANSI SQL without having to define complex schemata, or having to rebuild their entire data infrastructure. In this talk I will introduce the audience to Apache Drill—to include some hands-on exercises—and present a case study of how Drill can be used to query a variety of data sources. The presentation will cover: * How to explore and merge data sets in different formats * Using Drill to interact with other platforms such as Python and others * Exploring data stored on different machines

Apache Drill Workshop from Charles Givre

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0 Merlin: The Ultimate Data Science Environment /slideshow/merlin-the-ultimate-data-science-environment/62779010 merlinpresentation-160606172631
Merlin is a virtual computing environment developed by data scientists for data scientists. Merlin is free and open source, and contains a suite of all the best open source data science tools including data visualization tools, programming languages, big data tools, databases, notebooks, IDEs, and much more. The goal of Merlin is to allow data scientists to do data science work, not sysadmin.]]>
Merlin is a virtual computing environment developed by data scientists for data scientists. Merlin is free and open source, and contains a suite of all the best open source data science tools including data visualization tools, programming languages, big data tools, databases, notebooks, IDEs, and much more. The goal of Merlin is to allow data scientists to do data science work, not sysadmin.]]> Mon, 06 Jun 2016 17:26:31 GMT /slideshow/merlin-the-ultimate-data-science-environment/62779010 cgivre@slideshare.net(cgivre) Merlin: The Ultimate Data Science Environment cgivre Merlin is a virtual computing environment developed by data scientists for data scientists. Merlin is free and open source, and contains a suite of all the best open source data science tools including data visualization tools, programming languages, big data tools, databases, notebooks, IDEs, and much more. The goal of Merlin is to allow data scientists to do data science work, not sysadmin. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/merlinpresentation-160606172631-thumbnail.jpg?width=120&height=120&fit=bounds" /> Merlin is a virtual computing environment developed by data scientists for data scientists. Merlin is free and open source, and contains a suite of all the best open source data science tools including data visualization tools, programming languages, big data tools, databases, notebooks, IDEs, and much more. The goal of Merlin is to allow data scientists to do data science work, not sysadmin.

Merlin: The Ultimate Data Science Environment from Charles Givre

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0 Apache Drill and Zeppelin: Two Promising Tools You've Never Heard Of /slideshow/apache-drill-and-zeppelin-two-promising-tools-youve-never-heard-of/55138945 odscwest2015drillandzeppelin-151115225428-lva1-app6891
Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.]]>
Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.]]> Sun, 15 Nov 2015 22:54:28 GMT /slideshow/apache-drill-and-zeppelin-two-promising-tools-youve-never-heard-of/55138945 cgivre@slideshare.net(cgivre) Apache Drill and Zeppelin: Two Promising Tools You've Never Heard Of cgivre Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/odscwest2015drillandzeppelin-151115225428-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.

Apache Drill and Zeppelin: Two Promising Tools You've Never Heard Of from Charles Givre

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0 Strata NYC 2015 What does your smart device know about you? /slideshow/strata-nyc-2015-what-does-your-smart-device-know-about-you/54656203 stratanyc2015whatdoesyoursmartdeviceknowaboutyou-151102183403-lva1-app6891
Devices that make up the Internet of Things (IoT) collect a monumental amount of data about their owners. In most cases, the data they gather benefits the owner of the device and performs some useful purpose for them. However, when viewed in aggregate, the data gathered can reveal an enormous amount of information about the devices’ owner that can be very invasive if this information were to fall into the wrong hands. Over the course of several months, Charles Givre did an experiment in which he collected data from several IoT devices including a Nest Thermostat, the Automatic Car dongle, the Wink hub, and a few others in order to determine what could be learned about the owner of the devices. Givre approached this experiment like a law enforcement or intelligence investigation, beginning with a bit of seed knowledge about the target, and built a profile about the target using the data that was available via these devices’ APIs and the data they transmit over the internet. This presentation is not about how to bypass the devices’ security features, hack them, or how to mess with people by randomly turning off their A/C; but rather focuses on the consequences of IoT devices collecting and storing data.]]>
Devices that make up the Internet of Things (IoT) collect a monumental amount of data about their owners. In most cases, the data they gather benefits the owner of the device and performs some useful purpose for them. However, when viewed in aggregate, the data gathered can reveal an enormous amount of information about the devices’ owner that can be very invasive if this information were to fall into the wrong hands. Over the course of several months, Charles Givre did an experiment in which he collected data from several IoT devices including a Nest Thermostat, the Automatic Car dongle, the Wink hub, and a few others in order to determine what could be learned about the owner of the devices. Givre approached this experiment like a law enforcement or intelligence investigation, beginning with a bit of seed knowledge about the target, and built a profile about the target using the data that was available via these devices’ APIs and the data they transmit over the internet. This presentation is not about how to bypass the devices’ security features, hack them, or how to mess with people by randomly turning off their A/C; but rather focuses on the consequences of IoT devices collecting and storing data.]]> Mon, 02 Nov 2015 18:34:02 GMT /slideshow/strata-nyc-2015-what-does-your-smart-device-know-about-you/54656203 cgivre@slideshare.net(cgivre) Strata NYC 2015 What does your smart device know about you? cgivre Devices that make up the Internet of Things (IoT) collect a monumental amount of data about their owners. In most cases, the data they gather benefits the owner of the device and performs some useful purpose for them. However, when viewed in aggregate, the data gathered can reveal an enormous amount of information about the devices’ owner that can be very invasive if this information were to fall into the wrong hands. Over the course of several months, Charles Givre did an experiment in which he collected data from several IoT devices including a Nest Thermostat, the Automatic Car dongle, the Wink hub, and a few others in order to determine what could be learned about the owner of the devices. Givre approached this experiment like a law enforcement or intelligence investigation, beginning with a bit of seed knowledge about the target, and built a profile about the target using the data that was available via these devices’ APIs and the data they transmit over the internet. This presentation is not about how to bypass the devices’ security features, hack them, or how to mess with people by randomly turning off their A/C; but rather focuses on the consequences of IoT devices collecting and storing data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratanyc2015whatdoesyoursmartdeviceknowaboutyou-151102183403-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Devices that make up the Internet of Things (IoT) collect a monumental amount of data about their owners. In most cases, the data they gather benefits the owner of the device and performs some useful purpose for them. However, when viewed in aggregate, the data gathered can reveal an enormous amount of information about the devices’ owner that can be very invasive if this information were to fall into the wrong hands. Over the course of several months, Charles Givre did an experiment in which he collected data from several IoT devices including a Nest Thermostat, the Automatic Car dongle, the Wink hub, and a few others in order to determine what could be learned about the owner of the devices. Givre approached this experiment like a law enforcement or intelligence investigation, beginning with a bit of seed knowledge about the target, and built a profile about the target using the data that was available via these devices’ APIs and the data they transmit over the internet. This presentation is not about how to bypass the devices’ security features, hack them, or how to mess with people by randomly turning off their A/C; but rather focuses on the consequences of IoT devices collecting and storing data.

Strata NYC 2015 What does your smart device know about you? from Charles Givre

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0 https://cdn.slidesharecdn.com/profile-photo-cgivre-48x48.jpg?cb=1665773443 An innovative, resourceful, and self-motivated data scientist with 10 years of experience in the intelligence community in various organizations. I am passionate about solving difficult problems with data, and using data in unique ways to drive business decisions. Additionally, I enjoy teaching and mentoring. Specialties: PHP, Python, SQL, LAMP, JavaScript, ExtJS, Perl, CSS, HTML, Java, Analysis, Data Visualization, R, Pig, Hive, Hadoop Clearance: Top Secret / SCI with Full Scope Polygraph thedataist.com https://cdn.slidesharecdn.com/ss_thumbnails/apachecon2019-drillingsecuitydata-190326021713-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/drilling-cyber-security-data-with-apache-drill/138177036 Drilling Cyber Securit... https://cdn.slidesharecdn.com/ss_thumbnails/blockchainudfs-170926033814-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/blockchain-and-udfs/80159236 Blockchain and UDFs https://cdn.slidesharecdn.com/ss_thumbnails/dataexplorationwithapachedrill-day2-170301212305-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-exploration-with-apache-drill-day-2/72713653 Data Exploration with ...