Data Antipatterns

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Are you running a database in the cloud? Worried that you're doing it wrong? Engine Yard supports a broad set of databases with flexibility for customers to modify and configure. However, freedom to adapt and extend standard functionality comes with unexpected negative consequences: modifications can seriously affect durability and performance. I've observed common problems, patterns and best practices with big (and not so big) data. I'll highlight the most common pitfalls and discuss how to avoid them. Video for this talk is available here: http://vimeo.com/83755776

Technology

I work with
Databases

And I’m a
happy dog!

I
S

N
O M

E
B

S
R

A

ines @ Engine Yard.com
@Randommood

“I know you.
You know
you. And I
know you
know that I
know you”
White Goodman
(no relationship to White October)

Boring Deﬁnition #1

Copy and archiving
of data
Goal is to restore
the state of a DB

Backups

Many types - blah

Anti-Pattern #1

Not free, they
requires resources

Full backup every
hour, really?
Taking too
What about backup
many
backups retention?

Anti-Pattern #2

Taking too
few
backups

Enough to minimize
the risk of data loss
due to corrupted
backup files

yes,

Doing backups right

Logically
test
backups

Errorless restore
is not enough.
Test logical data
too

Doing backups right

Take logical and
binary backups
Continuous archiving
Know your
hot backup utilities
types
tools

Doing backups right

Practice
restores

Backups alone do not
constitute DR. Have a
plan practice it
Server extensions and
configuration matter
when restoring

“I want a
ridiculously
good
looking
Database”
Derek Zoolander
(honestly, Ben Stiller rules)

Anti-Pattern #4

Failure to
understand use
case, strengths
weaknesses of a
Cargo
new database
culting your
database

Anti-Pattern #5

Often means at least
one write per request
Tables have a
tendency to bloat

RDBMS for
Any DB issue/task may
Session
cause app to hang
Data

Anti-Pattern #6

Modeling, it’s all the same

Doing it right

Data Model
Consistency needs
Availability needs

Know your
needs

Scaling needs
Operational story
cost

Doing it right

Spike it,
forealsies

Spike it with your
data and traffic.
Best way to gain
operational
experience

Doing it right

Leverage
new
features

Relational databases
are getting quite
versatile
Evaluate clustered
MySQL options

We have a cloud deployment!
Happy team on shipping day, lmfao if you don’t celebrate like this

Obvious statement #2

Databases can live in
the cloud quite well
Many IaaS, PaaS,
DBaaS options

Cloud-based
databases,
they are real

Easy to get started
may be economical

Anti-Pattern #7

Where did my instance go?

Anti-Pattern #8

Cloud, it’s
just like
hardware

It’s not. Cloud
resources are
virtualized
Capacity planning
and monitoring
matter. A lot

Anti-Pattern #9

Shit doesn’t
happen

You are not
immune to
infrastructure
failures.
Plan for it

Anti-Pattern #10

Instance storage is
not persisted (use
EBS)
Data locality matters

Storage is
the same

Don’t run your cloud
DBs too hot!

Doing cloud right

Know your
cloud
deployments

Replication in the
cloud is a must-have
Put DB master
replicas in different
AZs

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DBX First Quarter 2024 Investor Presentation

Dropbox

In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

Ransomware_Q4_2023. The report. [EN].pdf

Overkill Security

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

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Artificial Intelligence Chap.5 : Uncertainty

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Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

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Data Antipatterns

1. DATA ANTI PATTERNS

2. I work with Databases And I’m a happy dog! I S N O M E B S R A ines @ Engine Yard.com @Randommood

3. Engine Yard

4. ZOMG, the horror!

5. .BACKUPS yes, we are going there

6. “I know you. You know you. And I know you know that I know you” White Goodman (no relationship to White October)

7. Boring Deﬁnition #1 Copy and archiving of data Goal is to restore the state of a DB Backups Many types - blah

8. Anti-Pattern #1 Not free, they requires resources Full backup every hour, really? Taking too What about backup many backups retention?

9. Anti-Pattern #2 Taking too few backups Enough to minimize the risk of data loss due to corrupted backup files yes,

10. this

11. totes

12. happens!

13. Anti-Pattern #3 The untested backup

14. Doing backups right Logically test backups Errorless restore is not enough. Test logical data too

15. Doing backups right Take logical and binary backups Continuous archiving Know your hot backup utilities types tools

16. Doing backups right Practice restores Backups alone do not constitute DR. Have a plan practice it Server extensions and configuration matter when restoring

17. “I want a ridiculously good looking Database” Derek Zoolander (honestly, Ben Stiller rules)

18. Obvious statement #1 Many DB choices

19. Anti-Pattern #4 Failure to understand use case, strengths weaknesses of a Cargo new database culting your database

20. Anti-Pattern #5 Often means at least one write per request Tables have a tendency to bloat RDBMS for Any DB issue/task may Session cause app to hang Data

21. Anti-Pattern #6 Modeling, it’s all the same

22. Doing it right Data Model Consistency needs Availability needs Know your needs Scaling needs Operational story cost

23. Doing it right Spike it, forealsies Spike it with your data and traffic. Best way to gain operational experience

24. Doing it right Leverage new features Relational databases are getting quite versatile Evaluate clustered MySQL options

25. We have a cloud deployment! Happy team on shipping day, lmfao if you don’t celebrate like this

26. Obvious statement #2 Databases can live in the cloud quite well Many IaaS, PaaS, DBaaS options Cloud-based databases, they are real Easy to get started may be economical

27. Anti-Pattern #7 Where did my instance go?

28. Anti-Pattern #8 Cloud, it’s just like hardware It’s not. Cloud resources are virtualized Capacity planning and monitoring matter. A lot

29. Anti-Pattern #9 Shit doesn’t happen You are not immune to infrastructure failures. Plan for it

30. Anti-Pattern #10 Instance storage is not persisted (use EBS) Data locality matters Storage is the same Don’t run your cloud DBs too hot!

31. Doing cloud right Know your cloud deployments Replication in the cloud is a must-have Put DB master replicas in different AZs

32. Doing cloud right Learn high availability disaster recovery Get good at replica promotions (some work involved) Understand and invest in DR/HA. Know your options

33. Doing cloud right Invest in monitoring Know baseline behavior Know your system Know your data distribution querying patterns

34. Questions?

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