Friday, January 13, 2017

Pre-Compare & Pre-Deployment Scripts to SSDT | the.agilesql.club

Pre-Compare & Pre-Deployment Scripts to SSDT | the.agilesql.club



  • 0. CI build runs pre-sqlpackage.exe script
  • 1. sqlpackage.exe compares dacpac to database
  • 2. sqlpackage.exe generates a deploy script
  • 3. sqlpackage.exe runs pre-deployment script
  • 4. sqlpackage.exe runs deploy script (generated in step 2)
  • 5. sqlpackage.exe runs post-deploy script
  • 6. CI build runs post-sqlpackage.exe script

Wednesday, January 11, 2017

Why Data Warehouse Projects Fail - Tim Mitchell

Why Data Warehouse Projects Fail - Tim Mitchell

How to reduce paging of buffer pool memory in the 64-bit version of SQL Server

SQL Server Latch & Debugging latch time out « MSSQLWIKI:

How to reduce paging of buffer pool memory in the 64-bit version of SQL Server



SQL Server and the “Lock pages in memory” Right in Windows Server | Glenn Berry's SQL Server Performance



Be Aware: Using AWE, locked pages in memory, on 64 bit – Slava Oks's WebLog





Interview with Alan Kay | Dr Dobb's

Interview with Alan Kay | Dr Dobb's















What .NET Developers ought to know to start in 2017 - Scott Hanselman

What .NET Developers ought to know to start in 2017 - Scott Hanselman

Why I told my friends to stop using WhatsApp and Telegram

Why I told my friends to stop using WhatsApp and Telegram



Telegram, the 100-million-user app made by social network VK’s founder Pavel Durov, uses its own encryption protocol: MProto. Telegram was the subject to a lot of controversies over its encryption protocol. Then in 2015, a security researcher published a paper revealing several major exploits in MProto and concluded that Telegram shouldn’t have tried to roll their own encryption.
So
this leaves us with WhatsApp and Signal — the only two applications to
use the Signal Protocol by default for all messages sent.
You may be asking — why not stick with WhatsApp then?
The reason lies in WhatsApp’s collection of metadata.

Building Resilient and Evolutionary Data Microservices

Building Resilient and Evolutionary Data Microservices





References

• Martin Kleppmann Schema Evolution in avro, thrift and protobufers:

https://martin.kleppmann.com/2012/12/05/schema-evolution-in-avro-protocol-buffers-thrift.html
http://dataintensive.net/ - Martin Kleppmann
• The CQRS Journey: https://msdn.microsoft.com/en-us/library/jj554200.aspx
• Oracle Datastore schema evolution :

https://docs.oracle.com/cd/NOSQL/html/GettingStartedGuide/schemaevolution.html
• Building Microservices by Sam Newman:

http://samnewman.io/books/building_microservices/
• Apache Avro: https://avro.apache.org/docs/1.7.7/gettingstartedjava.html
https://github.com/viniciusccarvalho/schema-evolution-samples

Tuesday, January 10, 2017

Microservices in Large Enterprises | @datawireio

Microservices in Large Enterprises | @datawireio



Despite its similarities to
SOA, microservices is not the same as SOA. There are tradeoffs to
consider in adopting microservices. Taking a page from the
agile manifesto and Richard Li’s framing of microservices,
one of the keys to microservices adoption in the enterprise is about
striking the balance between some key software engineering/ops, PMO and
QA functions especially in a large organization. I would propose the
following preferences:
  1. Deployment velocity over story point throughput
  2. Mean Time to Recovery over Mean Time Between Failure

Knee-Jerk Wait Statistics : PAGEIOLATCH_SH

Knee-Jerk Wait Statistics : PAGEIOLATCH_SH



One pattern to look for that would suggest a table/clustered index scan being the cause is also seeing a large number of CXPACKET waits along with the PAGEIOLATCH_SH waits. This is a common pattern that indicates large, parallel table/clustered index scans occurring.




In all cases, you could look at what query plan is causing the PAGEIOLATCH_SH waits using the sys.dm_os_waiting_tasks and other DMVs, and you can get code to do that in my blog post here.
If you have a third-party monitoring tool available, it may be able to
help you identify the culprit without getting your hands dirty.

Thursday, January 05, 2017

How to fix agile teams that are notoriously bad at hitting release dates | TechCrunch

How to fix agile teams that are notoriously bad at hitting release dates | TechCrunch



The project planning methods commonly used in non-IT engineering projects have the key to solving this problem. It’s called reference class forecasting.



Kahnemann and Tversky published a piece on intuitive prediction



our team’s process feature tags are derived from asking the following questions during estimation:


  •    Does external dependency on other teams exist?
  •    Do we depend on code that does not rely on the same standards (e.g. Ruby on Rails, APIs)?
  •    Is research needed to complete a problem?
  •    Do we have to write tests?
  •    Are there interdependencies within the code base?
  •    Are we touching new territory in some other way?

Magic Quadrant for Application Performance Monitoring Suites

Gartner Reprint



Gartner now defines APM
suites as one or more software and/or hardware components that
facilitate monitoring to meet three main functional dimensions:








  • Digital experience monitoring (DEM) —



    Digital experience monitoring is an availability and performance
    monitoring discipline that supports the optimization of the operational
    experience and behavior of a digital agent, human or machine, as it
    interacts with enterprise applications and services. For the purposes of
    this evaluation, it includes real-user monitoring (RUM) and synthetic
    transaction monitoring (STM) for both web- and mobile-based end users.








  • Application discovery, tracing and diagnostics (ADTD) —



    Application discovery, tracing and diagnostics is a set of processes
    designed to understand the relationships between application servers, to
    map transactions across these nodes, and to enable the deep inspection
    of methods and other host resources. It combines three formerly separate
    dimensions (application topology discovery and visualization,
    user-defined transaction profiling, and application component deep-dive)
    under a common name. All three dimensions are primarily focused on
    problem remediation and are interlinked.








  • Application analytics (AA) —



    Application analytics enables the automated detection of the source (or
    root cause) of performance anomalies for HTTP/S transactions supported
    by Java and .NET application servers through machine learning,
    statistical inference and/or other methods.




What matters to you, matters to us.

What matters to you, matters to us.



We focus on the small things because they matter to our customers. Being
an engineer at Slack means more than just submitting pull requests or
making changes because someone told you to. We’ve created a process and a
culture that encourages engineers to stay in touch with our customers
and empathize with their problems. We like to say that nothing is
someone else’s problem and we expect our engineers to take the attention
and care that our Customer Experience team does everyday. 

Wednesday, January 04, 2017

Monitoring and Optimizing Applications in AWS - Insight into how to improve IT visibility in AWS environments

Boston Amazon Web Services Meetup - Tuesday, 6:30-9PM - The Boston Amazon Web Services Meetup Group (Boston, MA) | Meetup







· Key challenges to managing cloud based apps, including pinpointing the origin of cloud latency


· Gaining insight into the geographic origin of user traffic service levels


· How to make more informed decisions about provisioning RDS and ELB


· How to effectively identify workloads to migrate to AWS


· Tips to optimize AWS based apps performance


· Detecting potential data leakage by monitoring use access to S3 buckets and files

Tuesday, January 03, 2017

Web bloat isn’t a knowledge problem | Christian Heilmann

Web bloat isn’t a knowledge problem | Christian Heilmann

It’s True! A Tale of Migrating to Amazon Aurora with no Interruption of Service - Nulab Inc.

It’s True! A Tale of Migrating to Amazon Aurora with no Interruption of Service - Nulab Inc.



We migrated the data from MySQL to Aurora using the replication function, following the user's guide: ''Migrating Data to Amazon Aurora DB Cluster.''
To do this, we made an Aurora instance from a snapshot of MySQL. Then
we migrated the MySQL data to Aurora by setting up replication with
MySQL as the master and Aurora as the slave.

Learn How North Carolina State University Handles E-Package Challenges | EBSCO post

Learn How North Carolina State University Handles E-Package Challenges | EBSCO post



offer a robust research collection of more than 4.6 million volumes,
including 70,000 print and electronic serial titles. The majority of
serial titles are within publisher packages.

  • ease of administering package title lists
  • minimizing exceptions and specialized workflows
  • efficient management of the billing and renewal cycle