Extending Queries & Reports with JSON & JQUERY
Presentation delivered as part of the Jedi track at the 2014 Slate Innovation Summit, June 26, 2014 in Hartford, CT.
Presenters: Jonathan Wehner and Bob McCullough
An introduction to data engineering & data science using Apache Spark and Java.
Get Spark in Action 2e, at http://jgp.ai/sia.
In this presentation, I start by loading a few CSV files in Spark (ingestion) and displaying them through the help of this new tool I build, dṛṣṭi.
As you can expect, I clean the data, join it, transform it, and continue to visualize it through dṛṣṭi.
I use Delta Lake to create a cache for my data and explain what imputation is and show I can use imputation on my datasets to add the missing datapoints.
I then use Spark on simple linear regressions to predict/forecast data.
dṛṣṭi is open source (Apache 2 license) and is available at: https://github.com/jgperrin/ai.jgp.drsti.
All the labs are available at https://github.com/jgperrin/ai.jgp.drsti-spark.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
MongoDB offers two native data processing tools: MapReduce and the Aggregation Framework. MongoDB’s built-in aggregation framework is a powerful tool for performing analytics and statistical analysis in real-time and generating pre-aggregated reports for dashboarding. In this session, we will demonstrate how to use the aggregation framework for different types of data processing including ad-hoc queries, pre-aggregated reports, and more. At the end of this talk, you should walk aways with a greater understanding of the built-in data processing options in MongoDB and how to use the aggregation framework in your next project.
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Attend this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo using it to analyze U.S. census data.
An introduction to data engineering & data science using Apache Spark and Java.
Get Spark in Action 2e, at http://jgp.ai/sia.
In this presentation, I start by loading a few CSV files in Spark (ingestion) and displaying them through the help of this new tool I build, dṛṣṭi.
As you can expect, I clean the data, join it, transform it, and continue to visualize it through dṛṣṭi.
I use Delta Lake to create a cache for my data and explain what imputation is and show I can use imputation on my datasets to add the missing datapoints.
I then use Spark on simple linear regressions to predict/forecast data.
dṛṣṭi is open source (Apache 2 license) and is available at: https://github.com/jgperrin/ai.jgp.drsti.
All the labs are available at https://github.com/jgperrin/ai.jgp.drsti-spark.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
MongoDB offers two native data processing tools: MapReduce and the Aggregation Framework. MongoDB’s built-in aggregation framework is a powerful tool for performing analytics and statistical analysis in real-time and generating pre-aggregated reports for dashboarding. In this session, we will demonstrate how to use the aggregation framework for different types of data processing including ad-hoc queries, pre-aggregated reports, and more. At the end of this talk, you should walk aways with a greater understanding of the built-in data processing options in MongoDB and how to use the aggregation framework in your next project.
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Attend this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo using it to analyze U.S. census data.
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
We will do a deep dive into the powerful query capabilities of MongoDB's Aggregation Framework, and show you how you can use MongoDB's built-in features to inspect the execution and tune the performance of your queries. And, last but not least, we will also give you a brief outlook into MongoDB 3.4's awesome new Aggregation Framework additions.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
We will do a deep dive into the powerful query capabilities of MongoDB's Aggregation Framework, and show you how you can use MongoDB's built-in features to inspect the execution and tune the performance of your queries. And, last but not least, we will also give you a brief outlook into MongoDB 3.4's awesome new Aggregation Framework additions.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
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Home assignment II on Spectroscopy 2024 Answers.pdf
Extending Slate Queries & Reports with JSON & JQUERY
1. EXTENDING QUERIES & REPORTS WITH
JSON & JQUERY
Case Western Reserve University
2. Tools
JSON (JavaScript Object
Notation):
• open standard
alternative to XML
• name/value pairs
• http://json.org
jQuery:
• open source JavaScript
library
• client side
• http://jquery.com
4. In Slate
select getdate() as [@updated],
(select f.[category] as [folder], f.[date] as [dtstart], f.[dtend], f.[deadline],
coalesce(f.[summary], p.[summary]) as [title],
coalesce(f.[location], p.[location]) as [location],
coalesce(f.[street], p.[street]) as [street],
coalesce(f.[city], p.[city]) as [city],
coalesce(f.[region], p.[region]) as [region],
coalesce(f.[postal], p.[postal]) as [postal],
coalesce(f.[country], p.[country]) as [country],
(select [name] from [world].dbo.[country] where ([id] = coalesce(f.[country],
p.[country]))) as [countryName],
convert(varchar, coalesce(f.[geo], p.[geo]).Lat) as [lat],
convert(varchar, coalesce(f.[geo], p.[geo]).Long) as [lng],
(select [value] from [config] where ([key] = 'https')) + '/register/'
+ coalesce(f.[alias] + '/', '?id=' + dbo.toGuidString(f.[id])) as [url],
(select f.[description] as [node()] for xml path('')) as [description]
from [form] f
left outer join [form] p on (p.[id] = f.[parent])
where (f.[type] = 'event') and (f.[date] > getdate()) and (f.[category] = 'Campus
Visit') and (f.[summary] != 'Campus Tour and Information Session')
for xml path('event'), type)
for xml path('events'), type
5. Output
{"events":{"@updated":"2014-06-19T14:47:40.963","event":[{"folder":"Campus Visit","dtstart":"2014-06-27T09:00:00","dtend":"2014-06-
27T12:00:00","title":"Campus Preview Day","location":"Thwing Center - Case Western Reserve University","street":"11111 Euclid
Ave","city":"Cleveland","region":"OH","postal":"44106-1715","country":"US","countryName":"United States","lat":"41.5074","lng":"-
81.6083","url":"https://go.case.edu/register/?id=7c2dcda2-7f40-4918-bcb6-97d60ef318ea","description":null},{"folder":"Campus Visit","dtstart":"2014-07-
11T09:00:00","dtend":"2014-07-11T12:00:00","title":"Campus Preview Day","location":"Thwing Center - Case Western Reserve University","street":"11111
Euclid Ave","city":"Cleveland","region":"OH","postal":"44106-1715","country":"US","countryName":"United States","lat":"41.5074","lng":"-
81.6083","url":"https://go.case.edu/register/?id=904b243a-7323-4f85-96df-3fe3babe15f9","description":null},{"folder":"Campus Visit","dtstart":"2014-07-
18T09:00:00","dtend":"2014-07-18T12:00:00","title":"Campus Preview Day","location":"Thwing Center - Case Western Reserve University","street":"11111
Euclid Ave","city":"Cleveland","region":"OH","postal":"44106-1715","country":"US","countryName":"United States","lat":"41.5074","lng":"-
81.6083","url":"https://go.case.edu/register/?id=fdf7e3f3-ba0a-4458-9d9d-203f21e9b95a","description":null},{"folder":"Campus Visit","dtstart":"2014-07-
25T09:00:00","dtend":"2014-07-25T12:00:00","title":"Campus Preview Day","location":"Thwing Center - Case Western Reserve University","street":"11111
Euclid Ave","city":"Cleveland","region":"OH","postal":"44106-1715","country":"US","countryName":"United States","lat":"41.5074","lng":"-
81.6083","url":"https://go.case.edu/register/?id=e29a3913-d467-40d1-bc4b-70b0d3b53487","description":null},{"folder":"Campus Visit","dtstart":"2014-
08-01T09:00:00","dtend":"2014-08-01T12:00:00","title":"Campus Preview Day","location":"Thwing Center - Case Western Reserve
University","street":"11111 Euclid Ave","city":"Cleveland","region":"OH","postal":"44106-1715","country":"US","countryName":"United
States","lat":"41.5074","lng":"-81.6083","url":"https://go.case.edu/register/?id=01a05b75-2a0b-4b24-8514-
596ea3c5597d","description":null},{"folder":"Campus Visit","dtstart":"2014-08-08T09:00:00","dtend":"2014-08-08T12:00:00","title":"Campus Preview
Day","location":"Thwing Center - Case Western Reserve University","street":"11111 Euclid Ave","city":"Cleveland","region":"OH","postal":"44106-
1715","country":"US","countryName":"United States","lat":"41.5074","lng":"-81.6083","url":"https://go.case.edu/register/?id=7b59f46a-7b15-4693-a7ed-
715f5657068a","description":null}]}}
10. In Slateselect p.[user],
/* Admitted Student Questions */
CASE
WHEN EXISTS(SELECT p.id
from [form.response] __r
inner join [form] __f on (__f.[id] = __r.[form])
inner join [person] p on (p.[id] = __r.[record])
where
(__f.[id] = '584465DF-AA36-4C2E-9481-D79E83221BB0') and (__r.[pending] = 0) and (__r.[active] = 1) and
(p.[user] = @cwrunetid)) THEN 1
ELSE 0
END AS 'SLAS',
...
/* Indians Game */
CASE
WHEN EXISTS(SELECT p.id
from [form.response] __r
inner join [form] __f on (__f.[id] = __r.[form])
inner join [person] p on (p.[id] = __r.[record])
where
(__f.[id] = 'F0DF5D33-A5AD-4CF3-95BC-FC0810F1F82D') and (__r.[pending] = 0) and (__r.[active] = 1) and
(p.[user] = @cwrunetid)) THEN 1
ELSE 0
END AS 'SLBB',
/* Final HS Transcript */
CASE
WHEN EXISTS( select p.[ref]
from [application] a
inner join [person] p on (p.[id] = a.[person])
where
((a.[id] IN (select a.[id] from [material] m inner join [application] a on (a.[person] = m.[record]) where
(m.[key] in ('ft')) and (m.[record] is not null) union all select [record] from [material] where ([key] in ('ft')) and
([record] is not null)))) and (p.[user] = @cwrunetid)) THEN 1
ELSE 0
END AS 'SLFT'
from [person] p
where (p.[user] = @cwrunetid)
for xml path('person'), type
13. Contact
Jonathan Wehner
Director of Recruitment and
Strategic Initiatives
jonathan.wehner@case.edu
@jonathanwehner
Bob McCullough
Director of Undergraduate
Admission
robert.mccullough@case.edu
@bobcwru