Data Services

Big Data Solutions

Process huge amounts of data fast with big data tools and technologies.

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Delivery focus

Built to move from idea to production.

When your data grows into millions of rows from many sources, spreadsheets stop working. As a big data services company, we use tools like Apache Spark, Hadoop, and Kafka to process huge amounts of data fast, so you spot trends before your competitors do. The work sits inside Data Services and is scoped, built, reviewed, and handed over as one engagement.

Apache Spark

Hadoop ecosystem

Kafka streaming

Real-time processing

Scope

ClickMasters builds big data infrastructure for B2B companies across the USA, Europe, Canada, and Australia. Apache Spark on Databricks or AWS EMR for distributed processing of terabyte to petabyte datasets. Apache Kafka for event streams at millions of events per second. Delta Lake and Apache Iceberg for data lakehouse architectures that combine the scale of object storage with ACID transaction guarantees. When your data has genuinely outgrown your SQL warehouse, we build the infrastructure that scales.

When Big Data Technology Is NOT the Right Solution

Big data infrastructure (Spark, Kafka, data lakehouse) is significantly more complex and expensive to build and maintain than standard SQL analytics. Do NOT adopt big data technology when: your data fits in a single Snowflake or BigQuery table under 1TB both can query this efficiently without Spark; your analytics team is small (fewer than 3-5 data engineers) the operational overhead of Kafka and Spark requires specialist expertise; or your bottleneck is data quality or business logic complexity rather than raw data volume. ClickMasters will tell you honestly when Snowflake or BigQuery can solve your problem and when you genuinely need Spark. The most common big data implementation mistake is using Spark to process 10GB of data that a single Postgres query would handle in 30 seconds.

Data Lakehouse vs Data Lake vs Data Warehouse

A data lake stores raw data in its native format (CSV, JSON, Parquet) on cheap object storage (S3, GCS) it is inexpensive, scalable, and flexible, but lacks ACID transactions, schema enforcement, and the query performance of a warehouse. A data warehouse (Snowflake, BigQuery) provides ACID transactions, schema enforcement, and fast analytical queries, but is more expensive per byte and less flexible for raw data formats. A data lakehouse combines both: it stores data in open table formats (Delta Lake, Iceberg) on cheap object storage, adding ACID transaction semantics (concurrent writes without corruption), schema enforcement (reject data that violates the schema), time travel (query historical states), and upserts/deletes (update or delete rows not possible with raw Parquet files). The result: the scale and cost of a data lake with the reliability and queryability of a data warehouse.

Databricks vs AWS EMR

Both Databricks and AWS EMR run Apache Spark, but they have different operational models. Databricks is a managed Spark platform (multi-cloud: AWS, GCP, Azure) with significant value-adds: Delta Lake as the native table format, Unity Catalog for data governance, collaborative notebooks with real-time co-editing, MLflow for experiment tracking, and the Photon native vectorised execution engine (2-5x faster than open-source Spark). Databricks charges a premium over raw cloud infrastructure costs, but reduces operational overhead significantly. AWS EMR is managed Hadoop/Spark on EC2 you get the infrastructure management handled (cluster provisioning, scaling), but without Databricks' platform layer. EMR is cheaper for steady, high-volume batch workloads where the team has strong Spark expertise. Databricks is better for teams that want to move faster, use Delta Lake natively, and reduce infrastructure management overhead. ClickMasters uses Databricks as the default for new Spark engagements.

Big Data Cost Management Five Levers

  • Cluster auto-termination: Spark clusters that run continuously when idle are the most common big data cost waste configure auto-terminate after 30-60 minutes of inactivity, spin up on schedule or trigger
  • Spot/preemptible instances: AWS Spot or GCP Preemptible instances for worker nodes 60-80% cheaper than on-demand, with automatic replacement on spot interruption appropriate for fault-tolerant batch workloads
  • Data partition pruning: Design partition schemes on S3/Delta Lake so queries only scan relevant partitions the single most impactful query cost optimisation
  • Caching: Spark RDD/DataFrame caching for iteratively queried datasets reduces recomputation
  • Storage tiering: S3 Intelligent-Tiering automatically moves infrequently accessed data to cheaper storage classes reduces long-term data lake storage costs by 30-40%

What this engagement delivers

Apache Spark (Databricks / AWS EMR)

Distributed data processing for large-scale workloads: PySpark DataFrame API (typed transformations, Catalyst optimiser), Spark SQL (SQL over DataFrames), Spark Streaming/Structured Streaming (micro-batch streaming, exactly-once semantics), Spark MLlib (distributed ML for datasets too large for scikit-learn). Deployment: Databricks (managed auto-scaling, Delta Lake native, Unity Catalog) or AWS EMR (managed Hadoop/Spark lower cost for steady workloads).

Data Lakehouse (Delta Lake / Iceberg)

Unified data platform combining data lake scale with data warehouse ACID guarantees: Delta Lake (ACID on Parquet, time travel, schema enforcement, MERGE INTO, Z-ORDER clustering), Apache Iceberg (Netflix/Apple multi-engine, same table queryable from Spark, Flink, Trino, Athena), Apache Hudi (Uber optimised for incremental ingestion).

Apache Kafka at Scale

High-throughput event streaming: Confluent Platform (managed Schema Registry, Kafka Connect, KSQL) or AWS MSK (managed Kafka), topic design (partition count, replication factor, retention), Kafka Connect (source/sink connectors), KSQL/Kafka Streams (stream processing in Kafka), Schema Registry (Avro/Protobuf backward/forward compatibility).

Real-Time Stream Processing

Sub-second event processing pipelines: Apache Flink (stateful event time windowing, exactly-once, stateful joins, the most capable open-source stream processor), AWS Kinesis Data Analytics (managed Flink), Spark Structured Streaming (micro-batch 100ms-1s latency, simpler than Flink). Use cases: real-time fraud detection (<100ms), live analytics aggregation, IoT sensor processing.

AWS Glue + S3 Data Lake

Serverless big data processing on AWS: AWS Glue (serverless Spark ETL pay-per-DPU-second), AWS Glue Data Catalog (centralised metadata accessible from Athena, Redshift Spectrum, EMR), Amazon Athena (serverless interactive SQL on S3 pay per bytes scanned, partition pruning essential), S3 Intelligent-Tiering (automatic cost optimisation).

Data Governance & Security

Enterprise data governance for large-scale data platforms: Unity Catalog (Databricks column-level access control, data lineage, PII tagging and masking, row-level security), Apache Ranger (policy-based access control), data masking (PII columns for non-production access), data lineage (OpenLineage + Marquez trace from raw source to BI dashboard, essential for GDPR).

Process

  1. Phase 1 · Week 1-2

    Big Data Architecture Review

    Volume assessment (TB/PB scale), velocity assessment (batch vs streaming), technology selection (Spark vs Flink, Delta vs Iceberg), cost model (Databricks vs EMR vs Glue), migration plan. Deliverable: Big Data Architecture Plan.

  2. Phase 2 · Week 2-5

    Spark / Databricks Setup

    Cluster configuration (auto-scaling, spot instances), Delta Lake setup, Unity Catalog (governance), notebook environment, PySpark/Spark SQL pipelines, optimisation (partitioning, caching, broadcast joins). Deliverable: Production Spark Platform.

  3. Phase 3 · Week 2-5

    Kafka Infrastructure

    MSK/Confluent cluster, topic design (partitions/replication), Kafka Connect (CDC Debezium, S3 sink), Schema Registry (Avro), KSQL/Kafka Streams applications, monitoring (latency, consumer lag). Deliverable: Streaming Platform.

  4. Phase 4 · Week 3-6

    Data Lakehouse Build

    Storage layer (S3/ADLS/GCS), Delta Lake/Iceberg table format, ACID transactions, time travel, Z-ORDER clustering, metadata catalog (Glue/Hive Metastore). Deliverable: Production Data Lakehouse.

  5. Phase 5 · Week 4-7

    Governance & Security

    Unity Catalog setup (Databricks) or Ranger (EMR), column-level access control, PII tagging and masking, data lineage tracking (OpenLineage), audit logging. Deliverable: Governed Data Platform.

Tech stack

Compute (Batch)
Apache Spark (Catalyst optimiser), Databricks (Photon engine 2-5x faster), AWS EMR, PySpark
Compute (Streaming)
Apache Flink (stateful, event-time), Kafka Streams (lightweight), Spark Structured Streaming (micro-batch)
Storage (Lakehouse)
Delta Lake (Databricks Z-ORDER), Apache Iceberg (Netflix/Apple), Apache Hudi (Uber)
Streaming Backbone
Apache Kafka (event streaming), AWS MSK (managed Kafka), Confluent Platform (Schema Registry, Connect, KSQL)
CDC
Debezium (PostgreSQL/MySQL to Kafka real-time), AWS DMS, Confluent CDC connectors
Serverless ETL
AWS Glue (serverless Spark pay-per-DPU-second), Athena (serverless SQL on S3)
Governance
Unity Catalog (Databricks column-level access, lineage, masking), Apache Ranger (policy-based), OpenLineage + Marquez (lineage)
Storage
AWS S3 (primary), GCS, ADLS, S3 Intelligent-Tiering (auto cost optimisation)

How we work

When Big Data is NOT Right

Amber callout Spark adds complexity without benefit for data <1TB | Basic: Spark for everything (overkill, expensive)

Databricks vs EMR Guide

Databricks for speed (Photon 2-5x faster, Delta native), EMR for cost (steady workloads, strong Spark expertise) | Basic: One-size recommendation

Delta Lake vs Iceberg vs Hudi

Delta Lake (Databricks native Z-ORDER), Iceberg (multi-engine Spark/Flink/Trino), Hudi (Uber incremental ingestion) | Basic: One lakehouse format

Flink for Real-Time

Sub-second latency with stateful event-time processing more capable than Spark Streaming | Basic: Spark Streaming only (1s latency, simpler but less capable)

Cost Optimisation

Auto-termination (idle clusters waste), spot instances (60-80% cheaper), partition pruning (single most impactful lever) | Basic: Always-on clusters (expensive waste)

Where this is used

Real-Time Fraud Detection

Kafka stream of transactions (100K/sec) → Flink real-time risk scoring (stateful, event-time windows) → alert within 100ms. Debezium CDC from PostgreSQL to Kafka for immediate transaction capture. Databricks for fraud pattern analysis on historical data.

IoT Sensor Processing

10,000+ IoT devices streaming to Kafka. Flink windowed aggregations (rolling 5-minute averages, anomaly detection). Delta Lake storage with time travel for sensor state reconstruction. Real-time alerts for threshold violations.

Clickstream Analytics Platform

100B+ click events/year from web/mobile to Kafka. Spark Structured Streaming for sessionisation. Delta Lake for ACID storage. Athena for ad-hoc analyst queries. Glue for ETL to Snowflake for BI.

Data Lakehouse Migration

Migrate from Hadoop HDFS (on-prem) to Delta Lake on S3. 500TB data, 10-year history. Z-ORDER optimisation for query performance. Unity Catalog for governance. Query performance improved 5x, costs reduced 60%.

Pricing guidance

These ranges are planning anchors. The quote names scope, timeline, and what is excluded. Talk through a quote.

Big Data Architecture Review

$5,000 - $10,000

1 - 2 weeks

Volume assessment, technology selection, cost model, migration plan

Spark / Databricks Setup

$12,000 - $35,000

4 - 8 weeks

Cluster config, Delta Lake, Unity Catalog, notebook environment

Kafka Infrastructure

$10,000 - $30,000

3 - 7 weeks

MSK/Confluent, topic design, Connect, Schema Registry, monitoring

Data Lakehouse (Delta/Iceberg)

$12,000 - $32,000

3 - 7 weeks

Storage layer, ACID transactions, time travel, query optimisation

Flink Stream Processing

$12,000 - $35,000

4 - 8 weeks

Stateful processing, event-time windows, exactly-once, deployment

AWS Glue + Athena Data Lake

$8,000 - $24,000

3 - 6 weeks

Serverless ETL, Glue Catalog, Athena queries, cost optimisation

Data Governance Layer

$10,000 - $28,000

3 - 6 weeks

Unity Catalog / Ranger, PII masking, lineage, access policies

Big Data Retainer

$5,000 - $12,000/mo

Ongoing

Cluster optimisation, new pipeline development, governance, monitoring

FAQs

When do I actually need big data technology like Spark?

The threshold where Spark becomes appropriate is roughly: data processing tasks that take more than 2-4 hours on a single machine (Spark's distributed processing splits the work across a cluster, reducing time proportionally), datasets larger than 1-2TB that make cloud data warehouse query costs prohibitive (Snowflake and BigQuery bill by bytes scanned a 10TB full table scan on BigQuery costs $50 each time), streaming requirements with sub-second latency across millions of events per second (standard SQL databases and even Kafka Streams have throughput limits), or ML model training on datasets too large for scikit-learn on a single machine (Spark MLlib distributes the training across a cluster). If your data fits in Snowflake or BigQuery and your queries complete in under 5 minutes, Spark adds complexity without benefit.

What is a data lakehouse and how is it different from a data lake or data warehouse?

A data lake stores raw data in its native format (CSV, JSON, Parquet) on cheap object storage (S3, GCS) it is inexpensive, scalable, and flexible, but lacks ACID transactions, schema enforcement, and the query performance of a warehouse. A data warehouse (Snowflake, BigQuery) provides ACID transactions, schema enforcement, and fast analytical queries, but is more expensive per byte and less flexible for raw data formats. A data lakehouse combines both: it stores data in open table formats (Delta Lake, Iceberg) on cheap object storage, adding ACID transaction semantics (concurrent writes without corruption), schema enforcement (reject data that violates the schema), time travel (query historical states), and upserts/deletes (update or delete rows not possible with raw Parquet files). The result: the scale and cost of a data lake with the reliability and queryability of a data warehouse.

What is the difference between Databricks and AWS EMR?

Both Databricks and AWS EMR run Apache Spark, but they have different operational models. Databricks is a managed Spark platform (multi-cloud: AWS, GCP, Azure) with significant value-adds: Delta Lake as the native table format, Unity Catalog for data governance, collaborative notebooks with real-time co-editing, MLflow for experiment tracking, and the Photon native vectorised execution engine (2-5x faster than open-source Spark). Databricks charges a premium over raw cloud infrastructure costs, but reduces operational overhead significantly. AWS EMR is managed Hadoop/Spark on EC2 you get the infrastructure management handled (cluster provisioning, scaling), but without Databricks' platform layer. EMR is cheaper for steady, high-volume batch workloads where the team has strong Spark expertise. Databricks is better for teams that want to move faster, use Delta Lake natively, and reduce infrastructure management overhead. ClickMasters uses Databricks as the default for new Spark engagements.

How do you manage costs for big data infrastructure?

Big data infrastructure cost management focuses on five levers. Cluster auto-termination (Spark clusters that run continuously when idle are the most common big datdata cost waste configure auto-terminate after 30-60 minutes of inactivity, spin up on schedule or trigger). Spot/preemptible instances (AWS Spot or GCP Preemptible instances for worker nodes 60-80% cheaper than on-demand, with automatic replacement on spot interruption appropriate for fault-tolerant batch workloads). Data partition pruning (design partition schemes on S3/Delta Lake so queries only scan relevant partitions the single most impactful query cost optimisation). Caching (Spark RDD/DataFrame caching for iteratively queried datasets reduces recomputation). Storage tiering (S3 Intelligent-Tiering automatically moves infrequently accessed data to cheaper storage classes reduces long-term data lake storage costs by 30-40%). ClickMasters implements monitoring dashboards for all big data engagements daily cost per pipeline and cluster with budget alerts.

Related reading

Big Data Solutions is one line inside Data Services. Teams with an adjacent problem often compare it with Data Science & Analytics and Business Intelligence (BI). For a written delivery narrative, read the SaaS booking platform QA case study and the rest of the case studies.

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