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Data Lakehouse: It's a design choice, not an evolution

To fit in your need and move forward with agility.

Posted on February 15, 2024

Data Lakehouse is a relatively new term, but not a new desire. It’s about flexibility, structure, and long-term scalability, all consolidated into an architecture. [Read More]
Tags:
- data engineering
Why DBT and How Does It Help?

Compared to Conventional Ways to Build Data Models

Posted on January 5, 2024

You have probably heard the buzz about DBT in data engineering lately, but why was DBT even invented in the first place? I was struggling with pure SQL scripts but now I have been using it as my day-to-day work, let me share you how it’s revolutionizing the way we... [Read More]
Tags:
- data engineering
- data analytics
Change Data Capture (CDC) pattern: another real-time data ingestion approach.

To listen all the changes.

Posted on November 20, 2023

There are various ways to conduct data ingestion, depending on the strategy we want to use the ingested data. Change Data Capture (CDC) is one approach to implement data ingestion at low latency, which is suitable for near real-time data consumption. [Read More]
Tags:
- data engineering
Understanding Connection Pooling

Simple but effective

Posted on August 11, 2023

Connection pooling is a simple technique that can significantly improve the performance of your application. However, to effectively apply and manage connection pooling, it is crucial to understand how it works and how it can be configured. [Read More]
Tags:
- engineering
My Journey as an MLOps Engineer

Challenging but rewarding

Posted on March 29, 2023

In today’s data-driven world, the demand for machine learning models has skyrocketed, leading organizations to invest heavily in their AI capabilities. However, building and deploying machine learning models efficiently is not a straightforward task. This is where MLOps (Machine Learning Operations) engineers come into play. In this blog post, I... [Read More]
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Lessons learned contributing to Open Source projects

Why don't you try?

Posted on March 2, 2023

To rapidly progress in your engineering career, it is advisable to learn from other engineers in your professional circle, such as colleagues, seniors, and friends especially when you are working on some immature area like MLOps. However, many individuals, myself included, often wonder how engineers from diverse companies, organizations, and... [Read More]
Tags:
- career
- experience
Beyond Feature Store: What we all desire

Yes, we need Feature Store, but how to use?

Posted on January 15, 2023

We have been working with Machine Learning models, cooking raw data into golden features for the models. Including me, we have been doing stuff manually and we also have been questioning where to put cooked features properly, like in our local machine? We have heard there is a Feature Store... [Read More]
Tags:
- mlops
- data engineering
Git commit convention: How to

Use your daily basis to power up your team

Posted on October 22, 2022

If you’ve been actively using git in your daily work, you might be familiar with the concept of Git commit convention. However, have you considered the potential benefits these messages hold for your future and the success of your team? In this article, I will explain how incorporating Git commit... [Read More]
Tags:
- engineering
- craftmanship
Dockerfile practices

Power your application with streamlined Dockerfiles

Posted on August 2, 2022

Docker has revolutionized the way we build, package, and deploy applications. It provides a lightweight and portable platform that enables developers to create isolated containers, encapsulating applications and their dependencies. One of the key components of Docker is the Dockerfile, a simple yet powerful tool for defining and automating the... [Read More]
Tags:
- mlops
- engineering

Data Lakehouse: It's a design choice, not an evolution

To fit in your need and move forward with agility.

Why DBT and How Does It Help?

Compared to Conventional Ways to Build Data Models

Change Data Capture (CDC) pattern: another real-time data ingestion approach.

To listen all the changes.

Understanding Connection Pooling

Simple but effective

My Journey as an MLOps Engineer

Challenging but rewarding

Lessons learned contributing to Open Source projects

Why don't you try?

Beyond Feature Store: What we all desire

Yes, we need Feature Store, but how to use?

Git commit convention: How to

Use your daily basis to power up your team

Dockerfile practices

Power your application with streamlined Dockerfiles