Apache Spark : compute distribué 2026

Apache Spark reste l'engine compute distribué dominant pour big data en 2026. Databricks ($43B valuation, IPO retardé) pousse Spark + Delta Lake + Unity Catalog + Photon. Mais Polars (Rust) et DuckDB grignotent les use cases mid-scale.

TL;DR

- Spark : leader compute distribué big data depuis 2014.

- 2026 : Databricks Photon engine 12x plus rapide.

- Challenges : Polars (Rust), DuckDB single-node killer.

- Use cases : ETL massif, ML training, streaming.

Spark architecture

`

Driver (coordinator)

↓

Cluster Manager (YARN, Kubernetes, Standalone)

↓

Executors (workers)

↓

Tasks (units of work)

`

API typique 2026

`python

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, sum, count

spark = SparkSession.builder \

.appName("sales_analysis") \

.config("spark.sql.adaptive.enabled", "true") \

.getOrCreate()

df = spark.read.parquet("s3://bucket/sales/")

result = (df

.filter(col("country") == "SN")

.groupBy("category")

.agg(

count("*").alias("nb_orders"),

sum("amount").alias("revenue")

)

.orderBy(col("revenue").desc())

)

result.write.parquet("s3://bucket/marts/senegal_sales/")

`

Databricks 2026

• Photon engine : C++ rewrite, 12x faster vs JVM
• Delta Lake : ACID transactions sur object storage
• Unity Catalog : governance unified
• DBSQL : warehouse-style serverless
• MLflow integration : tracking, registry, deployment
• Mosaic AI : LLM training/serving

Pricing Databricks

• DBU (Databricks Unit) based
• Compute : $0.07-0.55/DBU selon tier
• Premium features : Photon, Serverless, AI/ML
• Africa : 100-10K$/mois startups, 100K+/mois enterprise

Alternatives 2026

Polars

• Rust DataFrame library
• 10-50x faster than Pandas single-node
• Lazy evaluation, query optimizer
• Memory-efficient
• Pas distributed (encore)
• Adoption massive 2024-2026

DuckDB

• OLAP embedded database
• "SQLite for analytics"
• Single-node mais 100GB+ ok
• Memory + disk spillover
• Adoption boom 2024-2026

Ray

• Python distributed framework
• ML training scaling
• Anyscale commercial

Dask

• Python parallel computing
• Dataframe Pandas-like distributed
• Adoption mid-tier

Snowflake Snowpark

• Compute in-warehouse
• Python/Java/Scala stored procedures

Pattern moderne 2026

Small data (< 50 GB) : DuckDB ou Polars suffisent, plus rapide que Spark setup overhead.

Medium data (50 GB - 5 TB) : Snowflake/BigQuery suffisent, dbt pour transformations.

Big data (5 TB+) : Spark / Databricks toujours pertinent.

Streaming : Spark Structured Streaming, Flink, Kafka Streams.

Use cases Africa

• Telcos : CDR processing 100s TB/jour
• Banking : transactions historiques, fraud detection
• Mobile money : reconciliation massive
• Satellite imagery : Earth observation Africa
• Healthcare : population genomics

FAQ

Q : Spark obsolète ?

R : Non, mais surutilisé. 80 % use cases peuvent maintenant DuckDB/Polars + warehouse moderne. Spark reste roi big data 5 TB+.

Q : Databricks vs Snowflake ?

R : Databricks meilleur ML + data engineering. Snowflake meilleur warehouse + BI. Convergence 2024-2026 brouille les cartes.

Conclusion

Spark 2026 : leader compute distribué big data, Databricks pousse innovations (Photon, Delta, Mosaic AI). Mais Polars / DuckDB killent les use cases mid-scale. Pour startups Africa, commencer DuckDB/Polars + warehouse, monter à Spark seulement si TB+ sustained.