What Is Apache Spark? A Practical Guide for Enterprise Data Teams

The quick answer

Apache Spark is an open-source distributed computing engine designed for large-scale data processing. It runs computations across a cluster of machines in parallel, which allows it to process datasets that are too large for a single machine — terabytes or petabytes — in a fraction of the time of sequential processing. Spark is the dominant engine for data engineering workloads on Databricks, and is also available on Azure HDInsight, Amazon EMR, and Google Dataproc. For most enterprise data teams, Spark is the right tool when you have large-scale transformation pipelines, ML workloads, or streaming data processing requirements that exceed what SQL-based warehouses handle efficiently.

How Spark works

Spark processes data by distributing it across a cluster of worker nodes and executing computations in parallel. A Spark job runs on a cluster of machines: a driver node (which coordinates the job) and worker nodes (which execute the computation). The driver breaks the job into smaller tasks and distributes them to workers; workers execute the tasks on their portion of the data and return results.

The core data structure in Spark is the **DataFrame** — a distributed collection of data organised into named columns, similar to a table in a SQL database or a dataframe in pandas. Spark DataFrames are lazy: operations on them are not executed until an action (like a write or a collect) triggers execution. This laziness allows Spark's query planner to optimise the execution plan before running it.

Spark's speed advantage over older distributed systems (particularly Hadoop MapReduce) comes from in-memory computation. Spark caches intermediate results in memory rather than writing them to disk between steps, which dramatically reduces I/O overhead for multi-step transformation pipelines.

**Languages**: Spark natively supports Python (PySpark), Scala, Java, R, and SQL. PySpark is the most widely used interface for data engineering; Spark SQL allows SQL queries against Spark DataFrames, which is familiar for analysts and analytics engineers.

Spark in the modern data stack

In most enterprise data platforms, Spark is positioned at the transformation layer — processing raw data from the Bronze layer (cloud object storage) into cleaned and enriched Silver and Gold layers. The typical architecture:

**Ingestion** → raw data lands in ADLS Gen2, S3, or GCS via Fivetran, ADF, or Kafka

**Spark/Databricks** → reads raw data, applies transformation logic (deduplication, type casting, business logic), writes to Delta Lake or Iceberg tables

**SQL layer** → Spark SQL, Snowflake SQL, or a BI tool connects to the Delta/Iceberg tables for analytics

**BI tools** → Tableau, Power BI connect to the SQL layer

Spark is at the processing layer, not the storage or serving layer. It is the engine that transforms data, not the warehouse that stores it for queries.

The key distinction: Spark is a **processing engine**, not a database. It does not serve ad-hoc SQL queries efficiently (there is no persistent server waiting to respond to queries — each Spark query starts a cluster, processes data, and returns results). For high-concurrency BI workloads where many users are running simultaneous queries, a SQL warehouse (Snowflake, BigQuery, Synapse) is the right serving layer. Spark handles the heavy transformation lifting; the warehouse handles the BI query serving.

PySpark vs Spark SQL vs dbt

Enterprise data teams working on Databricks or cloud-hosted Spark have three primary interfaces for writing Spark transformations:

**PySpark** — Python API for Spark. Full programmatic control: complex logic, ML feature engineering, custom UDFs, conditional processing. The right choice when transformation logic is too complex for SQL (multi-step stateful processing, ML feature computation, custom business logic that does not express naturally in SQL). Requires Python proficiency.

**Spark SQL** — SQL queries executed against Spark DataFrames. Familiar for SQL-proficient analysts and analytics engineers. Less flexible than PySpark for complex logic but sufficient for most data transformation requirements. Runs on the same Spark engine as PySpark — performance is equivalent.

**dbt with Databricks** — dbt runs SQL transformations on Spark/Databricks using the dbt-databricks adapter. dbt models are compiled to Spark SQL and executed on the cluster. This is the right interface for analytics engineers building governed data models with version control, testing, and documentation. dbt on Databricks gives you engineering discipline (testing, lineage, docs) for Spark SQL transformations that would otherwise be unmanageable at scale.

For most data engineering teams: PySpark for complex pipeline logic; dbt on Spark SQL for governed data models in the Silver and Gold layers.

Delta Lake: Spark's storage format

Delta Lake is an open-source storage layer that adds database-like capabilities to data stored in cloud object storage — ACID transactions, schema enforcement, time travel (querying data as it existed at a previous point in time), and efficient upserts (MERGE operations). Delta Lake is developed by Databricks and is the default storage format on the Databricks platform.

Why Delta Lake matters for Spark users:

**ACID transactions** — without Delta Lake, concurrent reads and writes to Spark-processed data in object storage can produce inconsistent results (a reader seeing partial writes). Delta Lake's transactional log ensures that reads always see a consistent snapshot of the data.

**Schema enforcement** — Spark without Delta Lake will happily write a DataFrame with a different schema to an existing table, silently corrupting it. Delta Lake enforces schema at write time, catching schema mismatches before they corrupt data.

**Efficient upserts** — updating existing records in Spark (rather than appending new records) requires either rewriting entire partitions or using Delta Lake's MERGE operation, which updates only the records that have changed. For CDC (change data capture) workloads and slowly changing dimensions, Delta Lake MERGE is essential.

**Time travel** — Delta Lake's transaction log makes it possible to query data as it existed at a previous point in time. For debugging (what did this table look like before the bad pipeline run?), compliance (what was the data as of this reporting date?), and machine learning (reproducible training datasets), this is genuinely valuable.

When to use Spark vs a SQL warehouse

Spark is not the right tool for every workload. Understanding where Spark wins and where a managed SQL warehouse is better prevents over-engineering.

Use Spark when:

- Processing datasets in the hundreds of gigabytes to petabytes range where SQL warehouse performance degrades

- Building ML feature engineering pipelines in Python that cannot be expressed in SQL

- Streaming data processing (Spark Structured Streaming)

- Complex transformation logic that requires Python-level control flow, custom functions, or ML library integration

- Reading from diverse source formats (JSON, Parquet, CSV, Avro) at scale without preprocessing

Use a SQL warehouse (Snowflake, BigQuery, Synapse) when:

- High-concurrency BI query serving — many simultaneous dashboard queries

- SQL-first analytics where the team's primary skill is SQL

- Governed self-service analytics where business analysts need direct access

- Operational simplicity is a priority — managed warehouses require less infrastructure expertise than Spark clusters

Use both (the most common enterprise architecture):

Databricks Spark for heavy transformation, ML, and complex pipelines → Delta Lake tables → Snowflake or Synapse for governed SQL analytics and BI serving. This is the architecture we build most frequently for enterprise organisations with both significant engineering and analytics workloads.

Spark performance: what matters

Spark performance is a specialised discipline. The most common performance issues:

**Shuffle operations.** When Spark needs to redistribute data across nodes (for joins, aggregations, groupBy operations), it performs a "shuffle" — moving data between workers over the network. Shuffles are expensive. Reducing shuffle volume (through partitioning, broadcast joins for small tables, and avoiding unnecessary wide transformations) is the highest-leverage performance optimisation.

**Data skew.** When data is unevenly distributed across partitions — one worker has 90% of the data while others have 10% — the slow worker becomes the bottleneck. Identifying and addressing data skew (through salting, repartitioning, or AQE — Adaptive Query Execution) is a common tuning requirement.

**Cluster sizing.** Undersized clusters (insufficient memory for the data volume) cause spill to disk, which degrades performance significantly. Oversized clusters waste money. Right-sizing requires understanding the data volume and transformation complexity for each job.

**Caching.** Spark DataFrames that are reused multiple times in a job should be cached (df.cache() or df.persist()) so that Spark computes them once rather than recomputing from source on each use. Uncached DataFrames that are referenced multiple times in a complex pipeline recompute from scratch each time — a common source of unnecessary compute cost.

**Delta Lake optimisation.** For Spark jobs reading from Delta Lake tables, OPTIMIZE (compacting small files into larger ones) and ZORDER (colocating related data) on the target table improve read performance significantly. Tables with many small files (common after streaming writes or frequent small-batch ingestion) read much more slowly than tables with properly sized files.

Spark and AI workloads

Spark's role in AI workloads goes beyond data preparation. Databricks' Spark platform includes:

**MLflow** — experiment tracking, model registry, and model serving. ML engineers log experiment parameters and metrics to MLflow during model training; the best model is registered in the MLflow Model Registry and served via a REST endpoint.

**Feature Store** — a governed store for ML features (pre-computed values used as model inputs). Features are computed by Spark pipelines and stored in the Feature Store, where they are versioned, documented, and accessible to any model training run. This ensures that the features used in training are identical to those used in production inference.

**Spark ML** — Spark's built-in machine learning library, with implementations of common algorithms (logistic regression, random forests, gradient boosting, clustering) that run distributed across the cluster. For large-scale ML workloads where scikit-learn is too slow on single-machine data, Spark ML scales horizontally.

For organisations building AI-ready infrastructure, Spark/Databricks provides the unified platform for data engineering (building the training data), ML engineering (building and deploying models), and analytics (serving the insights from those models) — from the same platform and governance layer.

FAQs

Do we need Spark if we already have Snowflake or BigQuery?

Possibly not. For organisations whose primary workloads are SQL analytics, governed BI reporting, and moderate-complexity transformations, Snowflake or BigQuery with dbt handles the transformation layer without requiring Spark. Spark adds value when: data volumes exceed what Snowflake/BigQuery handle efficiently, you have significant ML engineering requirements, or you need Python-level control for complex pipeline logic. If your team is SQL-first and your data volumes are moderate (< 1TB of daily processing), Snowflake + dbt is simpler to operate and sufficiently capable.

What is the difference between Spark and Hadoop?

Apache Hadoop's MapReduce engine processes data by writing intermediate results to disk between each step — which is slow for multi-step transformations. Spark performs most computation in memory, which is dramatically faster for iterative workloads (ML training, multi-step pipelines). Hadoop's HDFS storage is also largely superseded by cloud object storage (S3, ADLS, GCS). In modern data platforms, Spark on cloud object storage has replaced the Hadoop ecosystem for most workloads. HDFS and Hadoop MapReduce are legacy technologies; Spark is the current standard.

Should we use Databricks or cloud-managed Spark (EMR, HDInsight)?

Databricks provides the richest Spark environment: optimised Spark runtime (significantly faster than open-source Spark for many workloads), Delta Lake as a first-class citizen, Unity Catalog for governance, MLflow for ML tracking, and a notebook environment designed for data engineering and data science collaboration. Cloud-managed Spark (Amazon EMR, Azure HDInsight, Google Dataproc) provides more configuration control at lower cost, but requires more operational investment. For most enterprise teams adopting Spark, Databricks is the right starting point — the operational simplification and Spark optimisations justify the cost premium.

We have Python analysts who want to use Spark. Do they need to learn Scala?

No. PySpark — Spark's Python API — provides access to all Spark capabilities without Scala. Most Spark-based data engineering is done in PySpark today; Scala is used primarily by teams with existing Scala expertise or for performance-critical custom components. For analysts coming from pandas, PySpark's DataFrame API is similar in structure. The main adjustment is thinking about distributed execution (operations that work on a single machine in pandas may need to be restructured for distributed execution in PySpark), but the Python syntax is familiar.

Our cloud engineering practice designs and builds Spark-based data platforms on Databricks and Azure, including Delta Lake architecture, PySpark pipeline development, and MLflow integration. If your organisation is evaluating Spark or Databricks for your next data platform, book a free 30-minute audit and we will tell you whether Spark is the right fit for your workloads.

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