Alright, it’s time for the second post of our sequence focusing on AWS options to setup pipelines in a server-less fashion. The topics that we are covering throughout this series are:
In this post we complement the previous one, by providing infrastructure-as-code with Terraform for deployment purposes. We are strong believers of a DevOps approach also to Data Engineering, also known as “DataOps”. Thus we thought it would make perfect sense to share a sample Terraform module along with Python code.
To recap, so far we have Python code that, if triggered by a AWS event on a new S3 object, will connect to Redshift, and issue SQL Copy command statement to load that data into a given table. Next we are going to show how to configure this with Terraform code.
As usual, all the code for this post is available publicly in this github repository. In case you haven’t yet, you will need to install terraform in order follow along this post.
Continue reading “AWS Server-less data pipelines with Terraform to Redshift – Part 2”
This post is the first of sequence of posts focusing on AWS options to setup pipelines in a serverless fashion. The topics that we all cover throughout the whole series are:
In this post we lean towards another strategy to setup data pipelines, namely event triggered. That is, rather than being scheduled to execute with a given frequency, our traditional pipeline code is executed immediately triggered by a given event. Our example consists of a demo scenario for immediately and automatically loading data that is stored in S3 into Redshift tutorial. Continue reading “AWS Server-less data pipelines with Terraform to Redshift – Part 1”
Today is a short one, but hopefully a valuable devOps tip, if you are currently setting up remote logging integration to S3 of Airflow logs using Airflow version 1.9.0.
Basically this stackoverflow post provides the main solution. However, the current template incubator-airflow/airflow/config_templates/airflow_local_settings.py present in master branch contains a reference to the class “airflow.utils.log.s3_task_handler.S3TaskHandler”, which is not present in apache-airflow==1.9.0 python package. The fix is simple – use rather this base template: https://github.com/apache/incubator-airflow/blob/v1-9-stable/airflow/config_templates/airflow_local_settings.py (and follow all other instructions in the mentioned answer)
Hope this helps!
This blog post was originally published at GoSmarten website. As the number of projects where we use it was increasing, we thought we might as well share it. Let me know if it was helpful!
AWS offers the cool possibility to consume from Kinesis streams in real time in a serverless fashion via AWS Lambda. However in can become extremely annoying to have to deploy a Lambda function in AWS just to test it. Moreover, it is also expensive to hold a Kinesis stream (e.g. queue) up and running just to test code.
Thus, by combining Kinesis Client Library (KCL) with AWS Kinesis and DynamoDB docker containers we were able to recreate locally everything that happens on the background when you plug a Lambda function to a Kinesis stream on AWS. Besides saving costs, this allows developers to substantially reduce development time, as well as develop higher quality code due to the ease and flexibility of testing different scenarios locally.
Feel free to checkout the code supporting this blog post on our repository.
Context: Event Sourcing/CQRS
Event sourcing is not a new concept, but as available streaming technologies have evolved, its widespread use has gained the attention it deserves. Thanks to “publish-subscribe” type of queues, it has become much easier to build streams of events available to multiple consumers at the same time. This democratization of access to an immutable, append-only stream of events is essential, as it separates the responsibility of modelling an event schema to a particular logic. It is also the reason why so many people either argue CQRS and event sourcing are the same thing, or have a symbiotic relationship. Continue reading “Consuming Kinesis Streams with Lambda functions locally”
This blog post is the second and final part of the post Using akka streaming for “saving alerts”. Part 1 is available here. In this part we enter the details on how the application was designed.
Full disclosure: this post was initially published at Bonial tech blog here. If you are looking for positions in tech, I would definitely recommend checking their career page.
Application Actor System
The following illustration gives you a schematic view of all the actors used in our application, and (hopefully) some of the mechanics of their interaction:
As previously mentioned, one could divide the application lifecycle logically into three main stages. We will get into more detail about each one of them next, but for now let us walk through them narrowly and try to map to our application Continue reading “Using Akka Streaming for “saving alerts” – part 2″
Full disclosure: this post was initially published at Bonial tech blog here, one my favorite companies at the heart of Berlin, and where I have been fortunate enough to be working for 2+ years as a freelance Data Engineer. If you are looking for positions in tech, I can’t help to recommend checking their career page.
Some months ago I was working on an internal project at Bonial using Akka Streaming (in scala) to provide additional features to our current push notification system. The main goal of the project was to enhance the speed to which the client is able to notify its end users of available discount coupons. In this case, we wanted to notify the users in a real time fashion of available coupons on store, so that they could use them more effectively on the spot and save money. Hence our project code name: “saving alerts”!
After some architectural discussions where we compared several technical options, we decided to give akka streaming a go. It has been a fun ride, so we thought we might as well share some of the lessons learned. Continue reading “Using Akka Streaming “saving alerts” – part 1″
This post is part of a tutorial series:
- Getting through Deep Learning – CNNs (part 1)
- Getting through Deep Learning – TensorFlow intro (part 2)
- Getting through Deep Learning – TensorFlow intro (part 3)
Alright, lets move on to more interesting stuff: linear regression. Since the main focus in TensorFlow, and given the abundancy of online resources on the subject, I’ll just assume you are familiar with Linear Regressions.
As previously mentioned, a linear regression has the following formula:
Where Y is the dependent variable, X is the independent variable, and b0 and b1 being the parameters we want to adjust.
Let us generate random data, and feed that random data into a linear function. Then, as opposed to using the closed-form solution, we use an iterative algorithm to progressively become closer to a minimal cost, in this case using gradient descent to fit a linear regression. Continue reading “Getting through Deep Learning – Tensorflow intro (part 3)”