A case for ELT (i.e. extract, load, and transform) and difference between ETL and ELT

Can machine learning-based data structures i.e. learned data structures replace traditional data structures? This is a question recently asked and explored by a team of Google researchers led by Jeff Dean with a major focus on database indexes. Jeff is a Google Senior Fellow in the Google Brain team and widely known as a pioneer in artificial intelligence (AI) and deep learning community.

At re:Invent 2017, Amazon Web Services (AWS) announced Amazon Time Sync Service which provides a highly accurate and reliable global time reference. This service is immediately available in all AWS regions. Amazon Time Sync Service In many ways, Amazon Time Sync Service is truly inspired by Google’ TrueTime. TrueTime is a global reference clock with a bounded non-zero error. TrueTime utilizes satellite-connected GPS and atomic clocks.

Over the weekend, I took a shot to build something new in React. React is a JavaScript library to build user interfaces. React was open sourced by Facebook and since then it has gained popularity over other frontend frameworks such as Angular, Vue, etc. To start, I have a good experience in JavaScript and I have built several applications using Node, Express, and Angular. Unfortunately, React landscape is way too complex.

Kubernetes can be an ultimate local development environment particularly if you are wrangling with a large number of microservices. In this post, we will cover how you can create a local development workflow using Minikube and tools such as Make to iterate fast without the wait imposed by your continuous integration pipeline. With this workflow, you can code and test changes immediately.

Leadership as a service is not about extracting work or anything from teams but it’s about serving them and inspiring them by service.

In AI and machine learning, the future resembles the past and bias refers to prior information. There has been a growing interest in identifying the harmful biases in the machine learning. Often these harmful biases are just the reflection or amplification of human biases which algorithms learn from training data. Some training data sets such as text, medical, criminal, educational, financial etc.

In a recent blog post, we discussed object-inspired container design patterns in detail and the sidecar pattern was one of them. In a sidecar pattern, the functionality of the main container is extended or enhanced by a sidecar container without strong coupling between two. This pattern is particularly useful when using Kubernetes as container orchestration platform. Kubernetes uses Pods. A Pod is composed of one or more application containers.

In a recent article, Lake et al. ¹ examine what it means for a machine to learn or think like a person. They argue that contemporary AI techniques are not biologically plausible hence not scalable to the extent that will enable a machine to learn or think like a person. For instance, most neural networks use some form of gradient-based (e.g., backpropagation) or Hebbian learning.

The idea of Bloom Filter was conceived by Burton H. Bloom in 1970. In a nutshell, Bloom filter is a space-efficient probabilistic data structure normally used to check for set membership (i.e. Is element x in set S? ). Bloom filter offers incredible memory and run-time savings for set membership queries with a small margin of error i.e. false positives.

Computer science is full of complex hard-to-understand algorithms. Understandability of an algorithm - ability to explain an algorithm in simple terms - is generally under appreciated. Often poor understandability of an algorithm has a direct impact on practical real-world applications. In my opinion, if we can’t develop intuitions about algorithm it will be very difficult to implement or extend the algorithm for real-world use cases.