Apple’s Cheapest MacBook Neo Packs a Surprising Punch for Data Analysis

TL;DR: Can Apple’s most affordable MacBook handle serious database workloads? We put the MacBook Neo to the test with DuckDB and the results will shock you.

Apple’s newly released MacBook Neo has tech reviewers buzzing about its suitability for students, photographers, and writers. But there’s one critical question they’re not asking: Can this budget-friendly machine handle Big Data analytics? We decided to find out with a data-driven approach that would make any data scientist proud.

First Impressions: Less is More

When you buy this machine in the EU, prepare for minimalism. There’s no charging brick in the box—just the laptop and a braided USB-C cable. If you’re like most tech users, you probably have a few USB-C chargers lying around anyway.

The hardware customization is equally straightforward: you can only choose between 256GB or 512GB storage options. We opted for the 512GB model (bringing the price to $700 in the US or €800 in the EU) to handle our Big Data ambitions. The 8GB RAM is fixed, and there’s only one CPU option—but it’s a fascinating choice.

The Secret Weapon: Apple A18 Pro

This laptop is powered by the 6-core Apple A18 Pro, originally designed for the iPhone 16 Pro. We’ve actually tested this chip before under extreme conditions. In 2024, using DuckDB v1.2-dev, we discovered that the iPhone 16 Pro could complete all TPC-H queries at scale factor 100 in about 10 minutes when air-cooled, and in less than 8 minutes when submerged in dry ice.

The MacBook Neo should handle this workload easily—but could it do even more? Time for some benchmarking!

Benchmark Battle: MacBook Neo vs Cloud Giants

For our first experiment, we used ClickBench, an analytical database benchmark featuring 43 queries focused on aggregation and filtering operations. The dataset consists of a single wide table with 100M rows, requiring about 14GB when stored as Parquet.

We ported ClickBench’s DuckDB implementation to macOS and ran it on the MacBook Neo using the latest v1.5.0 release. Following our performance guide, we set a 5GB memory limit to reduce reliance on OS swapping and let DuckDB handle memory management for larger-than-memory workloads.

Our benchmark lineup included:

• MacBook Neo with 2 performance cores, 4 efficiency cores, and 8GB RAM
• c6a.4xlarge cloud instance with 16 AMD EPYC vCPU cores and 32GB RAM
• c8g.metal-48xl cloud instance with 192 Graviton4 vCPU cores and 384GB RAM

Each benchmark ran queries three times to capture both cold runs (first run with empty caches) and hot runs (subsequent runs with cached data).

The Shocking Results

Cold Run Performance: Here’s where things get wild. The MacBook Neo dominated with a sub-second median runtime, completing all queries in under a minute! The secret? Local NVMe SSD access versus network-attached storage on cloud instances. While the MacBook’s SSD isn’t top-tier (about 1.5GB/s), it’s dramatically faster than network storage for initial reads.

Hot Run Performance: In hot runs, the MacBook’s total runtime only improved by about 10%, while cloud machines showed their true power. The c8g.metal-48xl won by an order of magnitude with a total runtime of just 4.35 seconds. However, the MacBook Neo still beat the c6a.4xlarge on median query runtimes and was only about 13% slower overall despite the cloud box having 10 more CPU threads and 4x the RAM.

Pushing Further: TPC-DS Challenge

For our second experiment, we tested TPC-DS queries, which are more complex than the ubiquitous TPC-H benchmark. TPC-DS features 24 tables and 99 queries with advanced features like window functions.

Using DuckDB’s LTS version v1.4.4, we generated datasets with the tpcds extension and set a 6GB memory limit.

At scale factor 100, the MacBook breezed through most queries with a median runtime of 1.63 seconds and total completion time of 15.5 minutes.

At scale factor 300, things got interesting. While the median query runtime remained solid at 6.90 seconds, DuckDB occasionally used up to 80GB of space for spilling to disk. Query 67 took a whopping 51 minutes to complete, but the system persevered and completed all queries in 79 minutes.

The Verdict: Surprisingly Capable, But With Limits

Here’s the truth: if you’re processing Big Data workloads daily, the MacBook Neo isn’t your ideal choice. Yes, DuckDB runs beautifully on it and can handle substantial data through out-of-core processing. But the MacBook Neo’s disk I/O (1.5GB/s) pales compared to MacBook Air and Pro models (3-5GB/s), and 8GB of RAM will become a bottleneck over time.

If you need to process Big Data on the move and can stretch your budget, other MacBook models will serve you better. There are also excellent Linux and Windows options for mobile data processing.

However, if you primarily run DuckDB in the cloud and use your laptop as a client, this is an exceptional device. And you can rest easy knowing that when you occasionally need to crunch data locally, DuckDB on the MacBook Neo will absolutely deliver.

Bottom line: This budget MacBook Neo punches well above its weight class for data analysis, proving that you don’t always need the most expensive hardware to get serious work done.

Tags: #MacBookNeo #DuckDB #BigData #AppleA18Pro #DatabaseBenchmark #TechReview #DataScience #MachineLearning #CloudComputing #PerformanceTesting

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