Teach students how to identify an author's purpose with this interactive presentation. Designed specifically for intermediate and middle school students.
During this English lesson you will learn what fast food is and how to buy and order fast food at three of most common takeaways in the UK. The lesson shows several examples of how to order at a fast food restaurant.
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
As You Seek – How Search Enables Big Data AnalyticsInside Analysis
The Briefing Room with Robin Bloor and MarkLogic
Live Webcast on June 18, 2013
http://www.insideanalysis.com
The heart and soul of Big Data Analytics revolves around search. That's why we keep hearing about NoSQL database vendors aligning themselves with third-party search engines. Because these purpose-built database engines do not leverage the Structured Query Language, search is the means by which valuable insights are gleaned from them. But bolted-on search engines typically don't offer the kind of deep functionality that built-in engines can.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain how search functionality provides a window into the possibilities for Big Data Analytics. He'll be briefed by David Gorbet of MarkLogic who will tout his company's object database offering, which boasts more than 10 years of use in production. He'll discuss how search can be used to expose relationships in Big Data and thus help generate insights. He'll also provide details on MarkLogic's enterprise-caliber capabilities, such as ACID compliance, its SQL interface, and where semantics fit in the roadmap.
Teach students how to identify an author's purpose with this interactive presentation. Designed specifically for intermediate and middle school students.
During this English lesson you will learn what fast food is and how to buy and order fast food at three of most common takeaways in the UK. The lesson shows several examples of how to order at a fast food restaurant.
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
As You Seek – How Search Enables Big Data AnalyticsInside Analysis
The Briefing Room with Robin Bloor and MarkLogic
Live Webcast on June 18, 2013
http://www.insideanalysis.com
The heart and soul of Big Data Analytics revolves around search. That's why we keep hearing about NoSQL database vendors aligning themselves with third-party search engines. Because these purpose-built database engines do not leverage the Structured Query Language, search is the means by which valuable insights are gleaned from them. But bolted-on search engines typically don't offer the kind of deep functionality that built-in engines can.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain how search functionality provides a window into the possibilities for Big Data Analytics. He'll be briefed by David Gorbet of MarkLogic who will tout his company's object database offering, which boasts more than 10 years of use in production. He'll discuss how search can be used to expose relationships in Big Data and thus help generate insights. He'll also provide details on MarkLogic's enterprise-caliber capabilities, such as ACID compliance, its SQL interface, and where semantics fit in the roadmap.
Learn how we built search for Bitbucket Cloud using a Microservices approach, leveraging the foundation we shipped in Bitbucket Server. Hear about the challenges faced when building search for millions of users, building on top of the Bitbucket Connect platform, the approach the team took, and the lessons we learned.
Stefan Saasen, Bitbucket Architect, Atlassian
Off-Label Data Mesh: A Prescription for Healthier DataHostedbyConfluent
"Data mesh is a relatively recent architectural innovation, espoused as one of the best ways to fix analytic data. We renegotiate aged social conventions by focusing on treating data as a product, with a clearly defined data product owner, akin to that of any other product. In addition, we focus on building out a self-service platform with integrated governance, letting consumers safely access and use the data they need to solve their business problems.
Data mesh is prescribed as a solution for _analytical data_, so that conventionally analytical results (think weekly sales or monthly revenue reports) can be more accurately and predictably computed. But what about non-analytical business operations? Would they not also benefit from data products backed by self-service capabilities and dedicated owners? If you've ever provided a customer with an analytical report that differed from their operational conclusions, then this talk is for you.
Adam discusses the resounding successes he has seen from applying data mesh _off-label_ to both analytical and operational domains. The key? Event streams. Well-defined, incrementally updating data products that can power both real-time and batch-based applications, providing a single source of data for a wide variety of application and analytical use cases. Adam digs into the common areas of success seen across numerous clients and customers and provides you with a set of practical guidelines for implementing your own minimally viable data mesh.
Finally, Adam covers the main social and technical hurdles that you'll encounter as you implement your own data mesh. Learn about important data use cases, data domain modeling techniques, self-service platforms, and building an iteratively successful data mesh."
Data Engineering A Deep Dive into DatabricksKnoldus Inc.
During this session, you'll gain a comprehensive understanding of Databricks' capabilities for efficiently processing and managing data, with a focus on Apache Spark for data transformation. We'll cover data ingestion methods, storage, orchestration, and best practices to ensure your data engineering workflows are optimized for success.
Making Apache Spark Better with Delta LakeDatabricks
Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Delta Lake: Open Source Reliability w/ Apache SparkGeorge Chow
As presented: Sajith Appukuttan, Solution Architect, Databricks
Sept 12, 2019 at Vancouver Spark Meetup
Abstract: Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
Building an Observability Platform in 389 Difficult StepsDigitalOcean
Watch this Tech Talk: https://do.co/video_dworth
Dave Worth, Engineering Manager at Strava, lays out a strategy for choosing the right tech stack depending on your business and team need. Watch as he guides you through tool sets that navigate around business constraints and regulatory concerns.
About the Presenter
Dave Worth’s professional life consists of being a web and backend engineer who developed specialization in observability through building reliable distributed systems at Strava, and previously DigitalOcean. In his spare time, Dave loves cycling, jiu jitsu, and searching for another great math book to only read the first 50 pages of.
New to DigitalOcean? Get US $100 in credit when you sign up: https://do.co/deploytoday
To learn more about DigitalOcean: https://www.digitalocean.com/
Follow us on Twitter: https://twitter.com/digitalocean
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We're hiring: http://do.co/careers
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...Cambridge Semantics
Only with a rich and interactive semantic layer can your data and analytics stack deliver true on-demand access to data, answers and insights - weaving data together from across the enterprise into an information fabric. In this webinar we introduce Anzo Smart Data Lake 4.0, which provides that rich and interactive semantic layer to your data.
Analyze your Data Lake, Fast @ Any Scale - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
-Learn how to automatically discover, catalog, and prepare your data for analytics
-Understand how to query data in your data lake without having to transform or load the data into your data warehouse
-See how to analyze data in both your data lake and data warehouse
Time's Up! Getting Value from Big Data NowEric Kavanagh
The Briefing Room with Dr. Robin Bloor and CASK
We all know the promise of big data, but who gets the value? There are plenty of success stories already, and most of them involve one key ingredient: facilitated access to important data sets. Most research studies suggest that the Pareto principle applies: 80 percent goes to data integration, and only 20 to analysis. Inverting that balance is the Holy Grail.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why the time has finally come for turning the tables on the status quo in analytics. He'll be briefed by CASK CEO Jonathan Gray, who will showcase his company's big data integration platform, CDAP, which was specifically designed to expedite time-to-value for big data.
An examination of what drives us to choices, how we choose options, and how to be happy with our choices. Based on psychologists and behavioral economists such as Dan Pink, Dan Ariely, and Dan Gilbert.
2. Commercial Label Printing
Labels include information such as
barcodes, text, pictures, and RFID
Industries include: shipping, retail,
manufacturing, and pharmaceuticals
Users need solutions of scale; often
have many printers with few servers
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
3. Commercial Label Printing
Print Server
Requests
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
4. BarTender System 8.1
Print Server
Requests
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
5. Requirements for
BarTender 9.0
Perform all duties of a log file
Collect label data during prints
Store data to an SQL database
Reproduce previously printed labels
Provide tools for auditing print
activities
Make it easy
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
6. Technologies
C#
.NET 2.0
.NET Remoting
ADO.NET
WinForms
Microsoft SQL Server 2005
Full & Express Edition
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
7. BarTender System 9.0
User Audits
Collect
SQL Label Data
Database
Use Data to
Reproduce
Labels
Print Server
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
8. Quick Questions?
Seagull Scientifics' core products
provide automation and printing
Modern enterprises want robust data
security and detailed tracking
Customers also want reliability and
savings offered by reprinting
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
9. How does the user
interact?
?
System Service SQL
Database
Print Server
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
10. Role as Software Engineer
Design and develop user-interface
Implement .NET Remoting client and
client data access
Define schema required for reading
from SQL database
Optimize database performance
Document application for verification
by Quality Assurance
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
11. History Explorer
GM Format Sample from SeagullScientific.com
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
12. History Explorer
Requirements
Navigate through print jobs and
messages sent from applications
Filter, sort, and search
Provide preview of labels
Support reprint of logged labels
Deliver low-latency data access
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
13. BarTender Print Jobs
Provide complete history of labels
printed by BarTender application
Show originating computer and
printer, plus all unique job data
Link messages and job status
received during printing to each job
Enable users to easily find labels
Present an interface for label reprint
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
14. Application Messages
Provide history of messages
All message dialogs and some
background messages
Display information such as
originating application and computer,
plus message text
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
15. How do users understand
all this data?
Don’t overwhelm
Prioritize information
Build on proven concepts:
Microsoft Outlook
SQL Management Studio
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
16. Convert Data into
Information
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
17. How do users investigate
related information?
Be context-driven
Allow users to select level of detail
Show related data in close proximity
Status received from spooler
Messages received from BarTender
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
21. How do users identify
labels they have printed?
Text alone is not sufficient
Labels tend to all look the same
Dynamic information is often in a
barcode or a small amount of text
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
22. Labels Objects & Preview
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
23. How do users find
information efficiently?
‘When’ often easier than ‘What’
Offer many methods
Focus on the common, but allow for
the uncommon
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
24. Filtering By Column
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
25. Filtering by Time Span
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
26. Filtering by View
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
27. Filtering by Custom Criteria
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
29. How do users locate
specific labels?
Jobs can contain hundreds of labels
Filtering is not strong enough
Labels possess arbitrary information
in the form of “label objects”
Text often in the form of numbers
Barcodes with a “human-readable” value
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
31. Search!
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
32. How do users reprint?
Users need flexibility to reprint
All labels in a job
All labels on a page
Individual labels
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
33. Reprint By Job or Label
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
34. Summary
BarTender 9.1 added data collection
to commercial label printing suite
My primary role was developing the
History Explorer application
History Explorer was used to present
database information to users
With easy-to-use filtering and searching
Supporting reprint of jobs and labels
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
35. Thank You
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc
36. Questions?
Erik Ralston BarTender History Explorer
Software Engineer Seagull Scientific, Inc