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Data Science allows us to extract meaning from and interpret data.

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Apache Iceberg Table Optimization #10: The Endgame — Building an Autonomous Optimization Pipeline for Apache Iceberg

Apache Iceberg Table Optimization #10: The Endgame — Building an Autonomous Optimization Pipeline for Apache Iceberg

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3 min read
Apache Iceberg Table Optimization #9: Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery

Apache Iceberg Table Optimization #9: Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery

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3 min read
Apache Iceberg Table Optimization #8: Hidden Pitfalls — Compaction and Partition Evolution in Apache Iceberg

Apache Iceberg Table Optimization #8: Hidden Pitfalls — Compaction and Partition Evolution in Apache Iceberg

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3 min read
Apache Iceberg Table Optimization #2: The Basics of Compaction — Bin Packing Your Data for Efficiency

Apache Iceberg Table Optimization #2: The Basics of Compaction — Bin Packing Your Data for Efficiency

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3 min read
Apache Iceberg Table Optimization #5: Avoiding Metadata Bloat with Snapshot Expiration and Rewriting Manifests

Apache Iceberg Table Optimization #5: Avoiding Metadata Bloat with Snapshot Expiration and Rewriting Manifests

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3 min read
Apache Iceberg Table Optimization #4: Smarter Data Layout — Sorting and Clustering Iceberg Tables

Apache Iceberg Table Optimization #4: Smarter Data Layout — Sorting and Clustering Iceberg Tables

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3 min read
Apache Iceberg Table Optimization #7: Using Iceberg Metadata Tables to Determine When Compaction Is Needed

Apache Iceberg Table Optimization #7: Using Iceberg Metadata Tables to Determine When Compaction Is Needed

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3 min read
Apache Iceberg Table Optimization #3: Optimizing Compaction for Streaming Workloads in Apache Iceberg

Apache Iceberg Table Optimization #3: Optimizing Compaction for Streaming Workloads in Apache Iceberg

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3 min read
Apache Iceberg Table Optimization #1: The Cost of Neglect — How Apache Iceberg Tables Degrade Without Optimization

Apache Iceberg Table Optimization #1: The Cost of Neglect — How Apache Iceberg Tables Degrade Without Optimization

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3 min read
Exploring Japanese Winery Tech: Rain-Cut Systems and Overhead Canopies in Yamanashi Vineyards

Exploring Japanese Winery Tech: Rain-Cut Systems and Overhead Canopies in Yamanashi Vineyards

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2 min read
🛒 Real-Life Data Lakehouse Use Case: Revolutionizing Retail Analytics

🛒 Real-Life Data Lakehouse Use Case: Revolutionizing Retail Analytics

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2 min read
DeadLock - dead.lock file

DeadLock - dead.lock file

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1 min read
A Deep Dive into Clustering for Customer Segmentation

A Deep Dive into Clustering for Customer Segmentation

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4 min read
How Excel is Used in Real-World Data Analysis

How Excel is Used in Real-World Data Analysis

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2 min read
Machine learning and AI: the new trend

Machine learning and AI: the new trend

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3 min read
DeadLock - 66% Complete

DeadLock - 66% Complete

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1 min read
How Excel is Used in Real-World Data Analysis

How Excel is Used in Real-World Data Analysis

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2 min read
I’m Not a Genius — Just Simply Ambitious (And That’s Enough)

I’m Not a Genius — Just Simply Ambitious (And That’s Enough)

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2 min read
What are Vectors and Matrices?

What are Vectors and Matrices?

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3 min read
The Moral Compass of Machines: Ethical AI & Responsible Development

The Moral Compass of Machines: Ethical AI & Responsible Development

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3 min read
Streamlit Beginner Guide with Examples

Streamlit Beginner Guide with Examples

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3 min read
DeadLock - JSON Parsing #3

DeadLock - JSON Parsing #3

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1 min read
Pandas vs Polars: Is It Time to Rethink Python’s Trusted DataFrame Library?

Pandas vs Polars: Is It Time to Rethink Python’s Trusted DataFrame Library?

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How to Effectively Preprocess and Scale Your Data

How to Effectively Preprocess and Scale Your Data

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3 min read
The Cloud: Powering the Next Generation of Artificial Intelligence

The Cloud: Powering the Next Generation of Artificial Intelligence

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3 min read
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