Spark specialists for your dedicated team.

Where Spark fits in a long-term product team.

Spark performance is mostly a conversation about shuffles. Operations that need data grouped by key move it across the network, and that movement dominates runtime on any non-trivial job. Reducing shuffles, and making the ones that remain cheaper, is where nearly all real optimisation happens.

Data skew is the failure that looks like a hang. If one key holds a disproportionate share of rows, one task does most of the work while the cluster idles, and the job appears stuck at 99% for an hour. It is diagnosed by looking at task-level distribution, which is not where most people look first.

What your dedicated team does with Spark.

The most valuable Spark advice is often that a cluster is unnecessary. Data that fits comfortably in memory on one large machine is processed faster and far more cheaply by a single-node tool, and a surprising amount of production Spark is running on datasets that would.

Saying that before tuning the cluster is the right call, and it comes from specialists who know the real data volumes. That continuity is what a dedicated software development team provides.

Common Spark use cases.

• Shuffle-heavy jobs made tractable Partitioning and broadcast joins applied where the network movement is the cost.
• Skew diagnosed and fixed Task-level distribution examined, so a stuck job is explained instead of restarted.
• Right-sizing the tool to the data Single-node processing where the dataset does not justify a cluster.

Adding Spark skills to your team.

Spark work sits with a dedicated data engineering team, who check whether a cluster is needed before tuning one.

Spark specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.