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How Agoda builds efficient Data Warehouse with Vertica

How Agoda builds efficient Data Warehouse with Vertica

At Agoda, it is important for us to be able to analyze enormous amounts of data in real-time and extract significant value from it.
Go to the profile of Panatchakorn.Tor
Panatchakorn.Tor
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How AQE Helps Optimize Shuffle Partition Number

How AQE Helps Optimize Shuffle Partition Number

Apache Spark is swiftly gaining traction as a versatile data processing system when it comes to big data tools. It is especially useful for…
Go to the profile of Songkunjump
Songkunjump
10 Common Data Visualization Pitfalls to Avoid

10 Common Data Visualization Pitfalls to Avoid

Data is powerful. It presents valuable chance and it also can mislead the truth. It is necessary that we accurately present our data
Go to the profile of Yanisa Treesak
Yanisa Treesak
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Adaptive Load Shedding

Adaptive Load Shedding

This blog aims to understand why services need to be adaptive and talks about the different types of adaptive services.
Go to the profile of Sushmi Shrestha
Sushmi Shrestha
How to efficiently store and query hundreds of String Int dictionaries on one machine

How to efficiently store and query hundreds of String Int dictionaries on one machine

This article will cover the evolution of data structures that we are using while querying from the historical part of Agoda’s in-house…
Go to the profile of Evgeniy Zuykin
Evgeniy Zuykin
Managing and scaling different white label development and testing environments

Managing and scaling different white label development and testing environments

White-labelling (Part 2): how Agoda manages development environments for white labels
Go to the profile of Thammarith
Thammarith
Building and scaling different travel websites with one codebase

Building and scaling different travel websites with one codebase

White-labelling (Part 1): the secret tool that enables Agoda to build multiple travel websites for our partners
Go to the profile of Thammarith
Thammarith
Improving Database Development, CI/CD with Storage Snapshot

Improving Database Development, CI/CD with Storage Snapshot

Database (DB) Development is a mandatory part of software development. However, DB development consumes a lot of effort and can cause…
Go to the profile of Kittikun Chartmala
Kittikun Chartmala
Supporting more than 200 payment methods globally

Supporting more than 200 payment methods globally

Fintech payments are an essential component of driving the business success of any e-commerce platform these days. In the recent past…
Go to the profile of Vivek Parmani
Vivek Parmani
Introducing the Agoda Widget: What is it, and why did we develop it?

Introducing the Agoda Widget: What is it, and why did we develop it?

Agoda’s recent update to version 9.34 comes with a new Widget feature. Widget is one of the features introduced by Apple on its iOS 14…
Go to the profile of Michael Abadi S.
Michael Abadi S.
The secrets of Agoda’s uptime — A day in the life of a NOC engineer

The secrets of Agoda’s uptime — A day in the life of a NOC engineer

A day in the life of a NOC engineer
Go to the profile of Rui Gong
Rui Gong
How We Standardized Our Design Workflow at Agoda

How We Standardized Our Design Workflow at Agoda

I still remember the time I just joined Agoda four years ago when our team was much smaller. Designers were all sitting in the same corner…
Go to the profile of Anita Lee
Anita Lee
Developing And Testing Against a Bunch of Microservices in Agoda

Developing And Testing Against a Bunch of Microservices in Agoda

In Agoda, we use a microservice architecture that is independently deployable, highly maintainable, and testable. That does not mean that…
Go to the profile of Kornchanok Jiravesayakul
Kornchanok Jiravesayakul
Codegoda 2021 Problem Editorials

Codegoda 2021 Problem Editorials

Official editorials for the problems in Codegoda 2021, a programming competition by Agoda.
Go to the profile of Nakkapat Boonsri
Nakkapat Boonsri
How our data scientists' petabytes of data is ingested into Hadoop (from Kafka)

How our data scientists' petabytes of data is ingested into Hadoop (from Kafka)

Writing a trillion messages a day can create a lot of challenges (& files)
Go to the profile of Akshesh Doshi
Akshesh Doshi
Adding Time Lag to Monitor Kafka Consumer

Adding Time Lag to Monitor Kafka Consumer

Being intelligent about measuring our Kafka consumer lags
Go to the profile of Jessie Tanaboriboon
Jessie Tanaboriboon
How Agoda manages 1.5 Trillion Events per day on Kafka

How Agoda manages 1.5 Trillion Events per day on Kafka

At Agoda, we send around 1.5 Trillion events per day through Apache Kafka. Learn how we manage the scale.
Go to the profile of Shaun Sit
Shaun Sit
Scaling Vulnerability Management across a thousand image repositories

Scaling Vulnerability Management across a thousand image repositories

Container hardening is a proven control for all organizations as part of a strong IT security strategy. One important component of…
Go to the profile of Jemuel Dalino
Jemuel Dalino
Automating versioning and releases using Semantic Release

Automating versioning and releases using Semantic Release

We have been using Semantic Release for over a year now and actively adopting the same for our other projects. With few easy steps, we can…
Go to the profile of Yasser Shaikh
Yasser Shaikh
Breaking the Monolith

Breaking the Monolith

Modular redesign of Agoda.com
Go to the profile of Vlad Batushkov
Vlad Batushkov
Happy ASP.NET Core Performance Optimization

Happy ASP.NET Core Performance Optimization

Whether you have an app with just a few users or hundreds of millions of users per day, you can always improve a user’s experience by…
Go to the profile of Ilya Nemtsev
Ilya Nemtsev
Deserializing Millions Of Messages Per Second Per Core

Deserializing Millions Of Messages Per Second Per Core

Step-by-step journey to the peak of performance
Go to the profile of Sergei Shubin
Sergei Shubin
PriceAggregator: An Intelligent System for Hotel Price Fetching (Part 2)

PriceAggregator: An Intelligent System for Hotel Price Fetching (Part 2)

TTL is a trade-off between price-accuracy and itinerary coverage. In this blog, we explain how to determine the TTL to maximise the…
Go to the profile of Zhang Jiangwei
Zhang Jiangwei
PriceAggregator: An Intelligent System for Hotel Price Fetching (Part 1)

PriceAggregator: An Intelligent System for Hotel Price Fetching (Part 1)

In this blog, we first explain how Agoda manages inventories and what are the challenges. Later, we explain scientifically how and why it…
Go to the profile of Zhang Jiangwei
Zhang Jiangwei
PriceAggregator: An Intelligent System for Hotel Price Fetching (Part 3)

PriceAggregator: An Intelligent System for Hotel Price Fetching (Part 3)

In this blog, we first explain how Agoda uses SmartScheduler to improve the bookings by pro-actively sending requests to suppliers…
Go to the profile of Zhang Jiangwei
Zhang Jiangwei
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