Category Archives: Cloud Computing

Is AWS too complex for small dev teams & startups?


I was discussing a server outage with a colleague recently. AWS had done some confusing things, and the team was rallying to troublehsoot & fix.

He made an offhand comment that caught my attention…

AWS is too complex for small dev teams. I’d recommend we host in a traditional datacenter.

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It’s an interesting point. For all the fanfare over Amazon, lost in the shuffle is the staggering complexity that we’re taking on. For small firms, this is a cost that’s often forgotten when we smell the on-demand cool-aid that is EC2.

Here are my thoughts…

1. Over 70 services offered

Everytime I login to the AWS console there’s a new service offering. Lambda & serverless computing. CodeDeploy, Redshift, EMR, VPC’s, developer tools, IOT, the list goes on. If you haven’t enabled MFA on your IAM accounts you’re not alone!

Also: Is Amazon too big to fail?

2. Still complex to build high availability

The song I hear out of Amazon is, we offer all the components for a high availability infrastructure. multiple availability zones, regions, load balancers, autoscaling, geo & latency dns routing. What’s more companies like Netflix have open sourced tools to help.

But at a lot of startups that I see, all these components are not in use, nor are they well understood. Many admins are still using Amazon like an old-school datacenter. And that’s not good.

Sometimes it seems that AWS is a patient in need of constant medication.

Related: Are we fast approaching cloud-mageddon?

3. Need a dedicated devops

As AWS becomes more complex, and the offering more robust, so too the need for dedicated ops. If you’re devs are already out of bandwidth, but you don’t quite have so much need for a fulltime resource a consultant may be an option. Round out the team & keep costs manageable.

If you’re looking for an aws solutions architect, we can help!

Check out: Does Amazon eat it’s own dogfood?

4. Orchestration involves many moving parts

Infrastructure as code offers the promise of completely versioning all your servers, configurations and changes. From there we can apply test driven development & bring a more professional level of service to our business. That’s the theory anyway.

In practice it brings an incredible number of new toolsets to master and a more complex stack besides. All those components can have bugs, need troubleshooting. This sometimes just kicks the can down the road, moving the complexity elsewhere.

It’s not clear that for smaller shops, all this complexity is manageable.

Also: 5 things toxic to scalability

5. Troubleshooting failed deployments

I was looking at a problem with a broken deploy recently. Turns out a developer had copy & pasted some code solution off the internet, possibly from a tutorial, and broke deployments to staging.

Yes perhaps this was avoidable, and more checks & balances can fix. But my thought is continuous integration & continuous deployments are not a panacea. More complexity brings a more complex web to unweave.

I sometimes wonder if we aren’t fast approaching cloud-mageddon?

Read: Why Airbnb didn’t have to fail?

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Why is Reddit’s CTO Martin Weiner special?

reddit cto martin weiner

I was reading the New Stack recently, and stumbled on Joab Jackson’s article about Reddit CTO Martin Weiner.

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He had some pretty on point observations about stable applications & predictability.

1. Because he should know

He was technical lead at Pinterest & now he’s CTO at Reddit. Those are pretty serious creds. But why wouldn’t he advocate the coolest new language, or baddest new NoSQL database?

Also: Does Amazon eat it’s own dogfood?

2. Because you can google boring tech

That’s right, picture yourself the ops team or developer who’s gotten paged in the middle of the night. You rub your eyes and look at the computer screen. You’re getting an error on MySQL. You dial up google & find the answer. You fix it & fall back to sleep!

“If it is 3 A.M., and your site is broken, because it will break, whatever the problem is with MySQL, the answer will up on Google”

Related: Did Dropbox have to fail?

3. Because you want predictability

New unproven technologies may solve old problems, but they’re also unpredictable. They break in new ways. They’re still immature. That’s dangerous.

What you really want is predictability & you get that from boring tech.

Read: Is Amazon too big to fail?

4. Because you can hire for it!

There are lots of technologies that have been around for a while, that are stable, reliable & *gasp* you can find people who know them!

“Python is a really mature tech. Everyone knows how to use it, and you can hire for it”

Also: Is Amazon Redshift a game changer?

5. Because everything breaks

While you’re discovering the coolest bleeding edge technology, and imaging the castles you can build, don’t forget that it will break at some point.

“If it breaks in the middle of the night, they wake up and fix it”

With boring tech, the fix is within reach.

Also: Is data your dirty little secret?

Get more. Grab our exclusive monthly Scalable Startups. We share tips and special content. Our latest Why I don’t work with recruiters

Does Amazon eat it’s own dog food (ahem…) or drink it’s own champagne?

laura grit aws amazon retail champagne

I was flipping through the AWS reddit channel and found this excellent presentation from RE:Invent by Laura Grit. She’s in charge of Amazon Retail, and worked very closely with teams on migrating to AWS. She goes in-depth on what that cost in terms of development, what it saved in terms of unused capacity, and surprisingly operational headaches.

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Laura’s a great speaker. I was surprised to find that Amazon Retails migration was similar to many of the customers I’ve worked with in New York. Often they take a hybrid approach where Direct Connect is key, allowing them to move over in a measured way.

What’s more she talks about how EC2 instances have different performance characteristics & applications typically need to be tuned for that world.

I learned a lot more, here are the highlights…

1. Hybrid cloud was key

Around 11:00 in the video she talks about AWS Direct Connect & VPC. These two technologies allow you to leverage AWS as a hybrid cloud, connecting to your existing datacenter. Scale elastically, but migrate in steps.

For example Amazon Retail did only webserver fleet in isolation.

Also: Is Amazon too big to fail?

2. Excite business & developers both

Around 18:20 …

“Moving the webserver fleet not only got the business excited about the cost savings & our ability to scale linearly, but also got developers excited about the operational load decrease that they had to burden.

Once benefits of this were shown to the rest of the company it actually jump started a wave of migrations to ec2 from inside amazon retail. And we found from a program perspective this is important. To find early migrations that benefit both the business & the developers because then they are both working together to figure out how to move their services to AWS.”

And she also pointed out an interesting bit abaout cultural change…

“You may choose to not migration the simplest service from inside your company, but instead one that will create a cultural change in the company & force more migrations automatically to AWS.”

Related: Are SQL Databases dead?

4. Expect application changes

Flip through to 27:47 and she talks about application changes for the new environment of the cloud.

“Don’t expect migrations to require no changes to your applications…
The webserver fleet was not lift & shift”

Also: Why Dropbox didn’t have to fail

5. Cloud not a panacea

Fast forward over to 37:10 and you’ll hear Laura talk about technical debt. That’s big.

“The cloud is not a universal panacea. It can’t coverup for messy engineering practices.
An example of this is availability. Design for failure is a fundamental design principle of amazon.”

Also: Are generalists better at scaling the web?

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Is demand for aws skills skyrocketing?

aws solutions architect trend

If Google trends is any indication, we’re heading for a serious skills shortage around AWS. If you’re a devops, sysop or systems administrator… don’t walk, run in this direction!

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I’ve pivoted a few times in my career, and knowing which way the wind blows is how I keep up with change. And right now it seems to be blowing into the cloud!

1. AWS datacenter growth is staggering

Also: Is Amazon too big to fail?

2. What I hear from recruiters

I’ve been hearing from more & more recruiters recently. And all they can talk about is redshift & AWS cloud solutions architects.

I think recruiters sit in a unique position & have the pulse of the market like nobody else does.

Related: 8 questions to ask an aws expert

3. Certification bandwagon

AWS is pushing hard to help sysops level up their skills. This can only help push adoption, but it’s also ideal for those who are ready to learn more about the cloud.

Read: When hosting data on Amazon turns bloodsport

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5 ways to level up as cloud expert

aws certified

Cloud computing is blowing up! But don’t take my word for it, read this recent NY Times piece: Tech companies clamor to entice cloud computing experts.

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Still don’t believe me? Get on the phone with a recruiter or two. They’ll convince you because they’ve got companies banging down the door looking for talent that is plainly in SHORT SUPPLY. And that’s the supply *you* want to be. 🙂

Check Gary’s Guide Jobs, or the ever popular Angel List Jobs. There’s also Stack Overflow jobs and many more.

1. Become a book reviewer

You’ve already got a technical background, and want to hone those skills. Take a look at technical book reviewing.

Manning is putting out some excellent technical books these days. Apply here to be a reviewer.

Also take a look at Pragmatic Bookshelf. They are are looking for reviewers too.

In either case you can expect to spend time reading a book chapter by chapter, as it’s written, offer strategic or layout advice, feedback on presentation, comprehension, and edits.

Also: When hosting data on Amazon turns bloodsport

2. Join an Open Source project

There are millions. Flip through github to some that you’re interested in. Contribute a bug fix or comment, reach out to the project leaders.

Afraid to dive in? Join one of the forums or google discussion groups, and lurk for a while. Ask questions, offer a helping hand!

Related: Is Amazon too big to fail?

3. Self-paced labs

Online education is blowing up, and for good reason. They get the job done & for the right price!

One of my favorites for AWS Certification is the A Cloud Guru courses. These offer lecture style introduction to all levels of AWS from Sysops Administration, Developer & Solutions Architect to Devops, Lambda & CodeDeploy.

The courses are priced right, and geared directly towards Amazon’s certifications. That helps you focus on the right things.

Amazon also partners with qwiklabs to offer courses geared towards getting certified. There are specific ones for the associate & professional certification, and many others besides.

You’ll need to signup for AWS Activate first, before you can use these qwiklabs. They offer you 80 credits right out of the gate.

For the next two weeks many of the courses are free! One thing I really like is they include a free temporary aws login for the students. That way there’s no risk of deploying infrastructure, and accidentally getting a big bill at the end of the month.

The labs though are more like reading documentation versus a nice video course lecture. So you the student have to do a lot more to get through it.

Read: Are we fast approaching cloud-mageddon?

4. Coursera, Khanacademy & Udemy

There’s a free class on Coursera called Startup Engineering by Balaji Srinivasan & Vijay Pande. Some pretty amazon material & lectures in here, and if you’re determined, it’s 12 weeks that will get you going on the right foot!

KhanAcademy has a great many courses on computer programming. Awesome and free stuff here. One particularly interesting is their hour of code. For those hesitant, that’s an easy way to jump in!

There is also udemy, which offers some great material on cloud computing. Notice that the certification courses are the same ones from A-Cloud Guru!

Also: Are SQL databases dead?

5. Interview tests

Apply to jobs. Even if you’re unsure if that is your dream job. Why? Because they often include a test to find out about your technical chops. Diving into these tests is a great way to push your own edge. You may do well, you may not. Learn where your weaknesses are.

I especially like the ones where you’re asked to login to a server, configure some things, write some code, and solve a real problem. Nothing beats a real-world example!

Also: Why dropbox didn’t have to fail?

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Sean Hull interviewed on the Doppler Cloud podcast

I recently got a chance to talk with Mike Kavis over at Cloud Technology Partners. It was fun to get away from the keyboard, and in front of the microphone for a change.

Join 32,000 others and follow Sean Hull on twitter @hullsean.

1. Docker

Docker is making deployments easier & easier. But as the pace accelerates, are we introducing vulnerabilities & scalability problems faster than we can fix them?

Also: Are generalists better at scaling the web?

2. Redshift

I’ve blogged that I don’t work with recruiters but I do chat with them regularly.

In a recent conversation a recruiter asked me:

“Why is it that suddenly everyone is looking for Redshift?”

I’m seeing the same trend. And if you look at Hadoop you might see why. Writing SQL queries against Redshift data is wildly simpler than writing EMR jobs for Hadoop.

Related: Why Dropbox didn’t have to fail?

3. Devops automation

These days I hear a lot of talk that all operations is software development. Are you still SSHing into boxes. You’re doing it wrong!

Read: When hosting data on Amazon turns bloodsport

4. Hardware solves all speed problems

Having performance problems? Scale out! Database slow, scale up! These days it seems the old short sighted way of thinking is back with a vengence. Throw hardware at the problem and kick the can down the road.

Also: How to hire a developer that doesn’t suck?

5. Amazon disrupting VC

During dot-com version one-point-oh, you’d need hundreds of thousands to buy hardware & software licenses to get an idea off the ground. That necessarily meant real VC money to get off the ground.

Amazon web services & on-demand computing has brought world class infrastructure to even the smallest startups. For just dollars, they can get started.

Now we’re seeing startups get going with micro investments from the likes of Angel List syndicates. Cutting traditional VCs right out of the equation.

Also: Is Amazon too big to fail?

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Are you getting errors building Amazon lambda functions? Don’t fret I got you!

aws lambda python

Why does Amazon make lambda functions so hard to create? Well my guess is that when you live at the bleeding edge you should expect to get scrapes!!

Everybody is trying to build lambda functions these days. And it’s no wonder. Once you get them running, Amazon takes care of all the infrastructure drudge work! So cool.

Join 32,000 others and follow Sean Hull on twitter @hullsean.

I’ve been spending some time trying to get answers out of AWS support, and let me tell you it’s no fun. Yes all this stuff is new technology, and nobody has expertise in it in the way say you might in Linux or Oracle or another technology that’s been around for a decade.

Still you’d hope the techs would have some clue. In the end it was a slog dealing with support, and I think I was the one teaching them!

I did find Matt Perry’s howto which is pretty good.

Hopefully my own notes can help someone, so read on!

1. No lambda_function?

The very first issue you’re gonna run into is if you name the file incorrectly, you get this error:

Unable to import module 'lambda_function': No module named lambda_function

If you name the function incorrectly you get this error:

Handler 'handler' missing on module 'lambda_function_file': 'module' object has no attribute 'handler'

On the dashboard, make sure the handler field is entered as function_filename.actual_function_name and make sure they match up in your deployment package.

If only the messages were a bit more instructive that would have been a simpler step, but oh well!

Also: Is Amazon too big to fail?

2. No module named MySQLdb

This is a very tricky one. I mean after all you just spent all this time building your deployment package specifically for lambda, so what gives??

"Unable to import module 'lambda_function': No module named MySQLdb"

Turns out when you use a virtualenv, files will be installed into proj/lib/python2.7/site-packages/ or lib64. However Lambda wants them in the root proj/ directory! So move them there. I know I know. Weird, but that’s what they want.

Related: When hosting on Amazon turns bloodsport

3. Can’t find libmysqlclient

If you’re using the MySQLdb library like I was, you’ll eventually bump into this error:

Unable to import module 'lambda_function': cannot open shared object file: No such file or directory

Turns out that /usr/lib/ needs to be COPIED from /usr/lib. Don’t do “mv” or your system won’t have the mysql lib anymore!

Related: Are SQL databases dead?

4. Use the Amazon Lambda environment

One thing the support pointed out is that AWS as *supported images* for lambda development.

After all the errors above were resolved, it’s not clear to me that the supported AMI’s are truly required. However if you’re hitting intractable problems building a properly lambda deploy, you might wanna look at building one of these boxes.

Read: Why dropbox didn’t have to fail

5. Build your lambda deploy package

Now let’s roll it all together. Here’s are all the steps to build your deploy package.

- SSH to the instance
- mkdir test
- virtualenv test
- source proj/bin/activate
- sudo yum groupinstall 'Development Tools'
- sudo yum install mysql
- sudo yum install mysql-devel
- pip install MySQL-python
- cd test
- emacs -nw
- add your code to that file
- save the
- mv proj/lib/python2.7/site-packages/* proj/
- mv proj/lib64/python2.7/site-packages/* proj/
- rm -rf proj/lib (don't need dist-packages in the deploy pkg)
- rm -rf proj/lib64 (don't need dist-packages
- zip -r *

Also: How to hire a developer that doesn’t suck?

6. Upload your code

Uploading your code via the AWS dashboard is fine when you’re first testing things. But after a while it’ll get tiring going in the front door.

Create a new lambda function by specifying the basics as follows:

aws lambda create-function \
--function-name testfunc1 \
--runtime python2.7 \
--role arn:aws:iam::996225510001:role/lambda_basic_execution \
--handler lambda_function_file.handler_name \
--zip-file file://

And when you want to update your function, do the following:

aws lambda update-function-code \
--function-name testfunc1 \
--zip-file file://

Also: How to deploy on EC2 with Vagrant

Good luck with lambda. Once you get past Amazon’s weak documentation it’s pretty cool to be in a serverless computing environment. Happy deploying!

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Why is everyone suddenly talking about Amazon Redshift?

par accel redshift

It seems like all I hear these days is Redshift, Redshift, Redshift!

I met up with a recruiter today. We talked about this & that. The usual. Then when he came to the topic of technology he said,

“yeah it seems as though suddenly everybody is looking for Redshift & Snowflake”

As I blogged about before, I don’t work with recruiters, I learn a lot from them.

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Luckily I got to cut my teeth on Redshift about a year ago. I was senior database engineer managing Amazon & MySQL RDS, and they wanted to build a data warehouse. Bingo!

Here’s the big takeaway from my discussion today. Recruiters have their fingers on the pulse!

1. We need an Amazon expert

Here’s what else I’m hearing everywhere. “We’re migrating to AWS, can you help?” Complexity & confusion around the new virtual networking, moving into the cloud, and tuning applications & components to get the same performance as before. All of these are real & present needs for firms.

Related: Is data your dirty little secret?

2. We need a Redshift expert

Amazon bought Par Accel, a bleedingly fast warehouse. It uses SQL. It looks like Postgres, and handles petabytes. You read that petabytes! It’s so good in fact that it seems a lot of folks are now dumping Hadoop.

Incredible as that sounds, Redshift is delivering *that* kind of speed on that kind of big data. Wow! What’s more you skip the whole Hadoop cycle of write, test, debug, schedule job, fix bugs, and stir. With SQL you bring back the iterative agile process!

Read: 5 cloud challenges I’m thinking about today

3. We need a Hadoop expert

Ok, for those enterprises who aren’t sold on Redshift yet, there is still a ton of Hadoop out there. And for good reason.

Apache Spark is also getting really big now too. It’s an easier to manage successor to Hadoop, based around much of the same concepts.

Also: 5 core pieces of the Amazon cloud puzzle to get your project off the ground

4. We need strong Python skills

Python is everywhere. Amazon’s command line interface is python based. You see it everywhere. If it’s not in your wheelhouse get it there!

Also: Why Dropbox didn’t have to fail

5. We need communicators

Another interesting thing the recruiter said

“I was surprised & a little shocked that you suggested we meet for coffee. Most developers are hard to get out to have a conversation with.”

Good communicators are as in-demand as ever! Being able to and happy to talk with people who aren’t deeply technical, and distill complex technical jargon into plain english. And do that with a smile too & enjoy it?

That’s special!

Also: Should we be muddying the waters? Use cases for MySQL & Mongodb

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Is data your dirty little secret?

data comparison cloud

While I was fumbling for the dictionary to figure out what polyglot persistence was, the CTO had decided to build a warehouse on Redshift.

“Everybody’s moving data there.” He declared. I looked on quizzically.

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“That’s a very new database engine”, I chimed in. “Lets do some testing”.

And there began an adventurous ride into the bleeding edge of Amazon’s new data service offerings!

1. The data scientist comes crying

Are our transactional database we were using Amazon RDS for MySQL. It’s a great managed service that eliminates some of the headaches of doing it yourself. I wrote about thisRDS or MySQL Use Cases.

We needed some way to get data over to Redshift. We evaluated AWS Data Pipeline, but it wasn’t realtime enough. In a pinch we decided on a service called Flydata. After weeks of effort to get it setup & administered we had it running smoothly.

I since discovered some pipelining solutions dedicated to Redshift such as Alooma – modern data plumbing, RJMetrics pipeline and Domo. I *did not* manage to get Tungsten working. It supports redshift on paper, but has a lot of growing up to do.

Until one data when the data scientist shows up at my desk. “We have problems in our data on redshift.”. I look back confused. “Are you sure? Can you tell me where you’re seeing that?” I respond.

Also: When hosting data on Amazon turns bloodsport

2. Deleted data reappears in Redshift!

He sends me over some queries, that I rerun myself. I see extra data in Redshift too, data that had been deleted in MySQL. Strange. We dig deeper together trying to figure out what’s happening.

We find that the tables with extra data are child tables of a parent where data was deleted. Imagine Citibank deletes a customer, they also want to delete the records for monthly bills. Otherwise those will just be hanging around, and won’t match up anymore with a parent record for a customer. In real life Citibank probably doesn’t delete like this but it’s a helpful example.

The first thing I do is open a ticket with Flydata. After all we hadn’t gotten any errors logged. Things *must* be running correctly.

After highlighting the severity of the issue, we setup a conference call with Flydata. Digging further they discover the problem. Child table data can’t get deleted on Redshift, because it doesn’t support ON DELETE CASCADE. Wait what?

Turns out Flydata makes use of the MySQL transaction log to move data. In mysql to mysql replication this works fine because downstream you also have MySQL. It also implements on delete cascade so those child records will get cleaned up correctly. Since Redshift doesn’t have this, there’s no way for Flydata to instruct Redshift what to do. Again I said, wait what?

My surprise wasn’t that a new unproven technology like Redshift had a lot of holes & missing features. My surprise was that Flydata was just silently ignoring the problem. No logged messages to tell the customer about inconsistencies. At least?

Related: Is Amazon too big to fail?

3. The problem – comparing data

As you might imagine, this is a terrible way to find out about data problems. As the person tasked with moving data between these systems, eyes were on me. My thought was, we chose a service-based solution, so they’re manage data movement. If there’s a problem, they’ll surely alert us.

From there the conversation became, ok, how do we figure out where all these data differences are? Is it widespread or isolated to a few tables? Can we mitigate it with changed queries? Cleanup on a daily basis? These are some questions that’ll immediately come to mind.

To answer them we needed a way to compare table data across different databases. This is hard to do within a homogenous environment where server versions & datatypes are likely to be the same. It is much more complicated when you’re working across heterogenous systems.

Read: 5 Reasons to move data to amazon redshift

4. Build some way to spot check data

Although this still doesn’t seem a solved problem, there are some tools. One way is to perform checksums on tables & rows. These can then be compared to find differences.

This drew me to find Jason Friedman’s
table hash script on Github. It can work across MySQL, Postgres & redshift. Pretty cool stuff if you ask me.

One problem remains. Databases are always in flux. As such you may find discrepancies based on data that hasn’t been moved yet. Data that’s just changed in the last few minutes.

If you refresh data nightly, you may for example be able to stop a slave to compare data at an instant in time.

Also: Is Redshift outpacing hadoop as the warehouse for startups?

5. The mentality: treat data as a product & monitor

Solving tough problems like these is a work in progress. What it taught me is that:

You should own your data pipeline

This allows you to be vigilant about monitoring, throw errors if data is different, and ultimately treat data as a product. Owning the pipeline will mean you can build monitoring around stages, and automate spot checks on your data.

You won’t get it perfect, but you want to know when it isn’t.

Also: 5 core pieces of the Amazon puzzle to get your project off the ground

Get more. Grab our exclusive monthly Scalable Startups. We share tips and special content. Our latest Why I don’t work with recruiters

5 tech challenges I’m thinking about today

fast fish

Technical operations & startup tech are experiencing an incredible upheaval which is bringing a lot of great things.

Join 32,000 others and follow Sean Hull on twitter @hullsean.

Here are some of the questions it raises for me.

1. Are we adopting Docker without enough consideration?

Container deployments are accelerating at a blistering pace. I was reading Julian Dunn recently, and he had an interesting critical post Are container deployments like an oncoming train?

He argues that we should be wary of a few trends. One of taking legacy applications and blindly containerizing them. Now we can keep them alive forever. 🙂 He also argues that there is a tendency for folks who aren’t particularly technical or qualified who start evangelizing it everywhere. A balm for every ailment!

Also: Is Amazon too big to fail?

2. Is Redshift supplanting hadoop & spark for startup analytics?

In a recent blog post I asked Is Redshift outpacing hadoop as the big data warehouse for startups.

On the one hand this is exciting. Speed & agile is always good right? But what of more Amazon & vendor lock-in?

Related: Did Dropbox have to fail?

3. Does devops automation make all of operations a software development exercise?

I asked this question a while back on my blog. Is automation killing old-school operations?

Automation suites like Chef & Puppet are very valuable, in enabling the administration of fleets of servers in the cloud. They’re essential. But there’s some risk in moving further away from the bare metal, that we might weaken our everyday tuning & troubleshooting skills that are essential to technical operations.

Read: When hosting data on Amazon turns bloodsport?

4. Is the cloud encouraging the old pattern of throwing hardware at the problem?

Want to scale your application? Forget tighter code. Don’t worry about tuning SQL queries that could be made 1000x faster. We’re in the cloud. Just scale out!

That’s right with virtualization, we can elastically scale anything. Infinitely. 🙂

I’ve argued that throwing hardware at the problem is like kicking the can down the road. Eventually you have to pay your technical debt & tune your application.

Also: Are SQL databases dead?

5. Is Amazon disrupting venture capital itself?

I’m not expert on the VC business. But Ben Thompson & James Allworth surely are. And they suggested that because of AWS, startups can setup their software for pennies.

This resonates loud & clear for me. Why? Because in the 90’s I remember startups needing major venture money to buy Sun hardware & Oracle licenses to get going. A half million easy.

They asked Is Amazon Web Services enabling AngelList syndicates to disrupt the Venture capital business? That’s a pretty interesting perspective. It would be ironic if all of this disruption that VC’s bring to entrenched businesses, began unravel their own business!

Also: Are we fast approaching cloud-mageddon?

Get more. Grab our exclusive monthly Scalable Startups. We share tips and special content. Our latest Why I don’t work with recruiters