Apache Beam Consulting and Support Services
Build scalable and portable data processing solutions tailored for modern enterprises with experts.
24×7 Support Services
Enterprise Assurance with SLA-Backed Support
Experienced Apache Beam Experts
The result is a portable, high-performance, production-ready data platform built to scale with your enterprise needs. If your pipelines need to operate at enterprise scale, you need the right Apache Beam partner from day one.
Delivering robust Apache Beam support services for seamless pipeline development, integration, and continuous performance tuning.
Pipeline Architecture and Solution Design
Every Beam deployment starts with the right foundation. Our architects assess your throughput and latency requirements, then design end-to-end pipeline topologies covering windowing strategies, side inputs, runner selection, and capacity planning, all purpose-built for your data scale.
Runner Migration and Multi-Runner Strategy
Moving between runners without breaking production pipelines requires deep portability expertise. Our engineers handle the full migration lifecycle from DirectRunner to Dataflow, Flink, or Spark, covering portability audits, output parity validation, and staged cutover so downstream consumers stay unaffected throughout.
Pipeline Deployment and Configuration
A pipeline is only as reliable as its configuration. Our team tunes autoscaling workers, parallelism settings, and I/O connectors across Dataflow, Flink clusters, and Spark environments, hardening every layer before a single job hits production.
Data Integration and Pipeline Engineering
Connecting Beam to a diverse data stack is where many teams get stuck. Our integration specialists configure Kafka, Pub/Sub, and Kinesis sources alongside BigQuery, Iceberg, and Parquet sinks, migrating existing transformation logic through Beam's portable SDK without touching core business code.
Data Lake and Lakehouse Architecture
Building a clean, consistent lakehouse takes more than storage. Ksolves positions Beam as the transformation backbone for cloud-native lakehouses, integrating Apache Iceberg, Delta Lake, and Apache Hudi. Data consistency across all three formats is enforced through sink-level idempotency and runner-specific exactly-once configurations.
Data Analytics with Apache Beam
Petabyte-scale data is only useful if teams can query it fast. Our engineers connect Beam-processed output to Trino, Dremio, BigQuery, and Spark SQL, with Beam SQL server-side aggregations built directly into the pipeline graph and Apache Superset dashboards wired for direct, low-latency access.
Beam Managed Services
Keeping pipelines healthy under growing data pressure requires continuous operational expertise. Ksolves provides 24x7 health monitoring, proactive hot-key and slow-stage detection, and capacity forecasting, backed by regular performance reviews and roadmap advisory to stay ahead of demand.
Pipeline Health Check and Performance Audit
Hidden bottlenecks in Beam pipelines are rarely obvious until they cost you. Our audit team examines transform graphs, runner configurations, and I/O layers to surface data skew, fusion gaps, trigger correctness issues, and shuffle inefficiencies, delivering a prioritized remediation plan at the close of every engagement.
Monitoring with Managed Grafana
Operational visibility starts with the right metrics in the right hands. Ksolves instruments Prometheus data from Flink, Spark, and Dataflow into custom Grafana dashboards built around your pipeline KPIs. Alerting rules for lag, watermark delay, and worker failures are connected to PagerDuty, OpsGenie, and Slack.
Security and Data Governance
Enterprise pipelines need security built in at every layer, not bolted on after. Ksolves secures your infrastructure with IAM-native runner authorization, Secret Manager credential injection, TLS-encrypted I/O connectors, and Cloud DLP PII masking transforms, meeting HIPAA, GDPR, and SOC 2 Type II requirements across every deployment.
SDK Upgrades and Patch Management
Beam SDK upgrades carry real risk without the right preparation. Our engineers execute staged upgrades with compatibility assessment, connector behavior validation, and integration testing across target runners, with a fully documented rollback plan in place before any upgrade touches production.
Pipeline Architecture and Solution Design
Every Beam deployment starts with the right foundation. Our architects assess your throughput and latency requirements, then design end-to-end pipeline topologies covering windowing strategies, side inputs, runner selection, and capacity planning, all purpose-built for your data scale.
Runner Migration and Multi-Runner Strategy
Moving between runners without breaking production pipelines requires deep portability expertise. Our engineers handle the full migration lifecycle from DirectRunner to Dataflow, Flink, or Spark, covering portability audits, output parity validation, and staged cutover so downstream consumers stay unaffected throughout.
Pipeline Deployment and Configuration
A pipeline is only as reliable as its configuration. Our team tunes autoscaling workers, parallelism settings, and I/O connectors across Dataflow, Flink clusters, and Spark environments, hardening every layer before a single job hits production.
Data Integration and Pipeline Engineering
Connecting Beam to a diverse data stack is where many teams get stuck. Our integration specialists configure Kafka, Pub/Sub, and Kinesis sources alongside BigQuery, Iceberg, and Parquet sinks, migrating existing transformation logic through Beam's portable SDK without touching core business code.
Data Lake and Lakehouse Architecture
Building a clean, consistent lakehouse takes more than storage. Ksolves positions Beam as the transformation backbone for cloud-native lakehouses, integrating Apache Iceberg, Delta Lake, and Apache Hudi. Data consistency across all three formats is enforced through sink-level idempotency and runner-specific exactly-once configurations.
Data Analytics with Apache Beam
Petabyte-scale data is only useful if teams can query it fast. Our engineers connect Beam-processed output to Trino, Dremio, BigQuery, and Spark SQL, with Beam SQL server-side aggregations built directly into the pipeline graph and Apache Superset dashboards wired for direct, low-latency access.
Beam Managed Services
Keeping pipelines healthy under growing data pressure requires continuous operational expertise. Ksolves provides 24x7 health monitoring, proactive hot-key and slow-stage detection, and capacity forecasting, backed by regular performance reviews and roadmap advisory to stay ahead of demand.
Pipeline Health Check and Performance Audit
Hidden bottlenecks in Beam pipelines are rarely obvious until they cost you. Our audit team examines transform graphs, runner configurations, and I/O layers to surface data skew, fusion gaps, trigger correctness issues, and shuffle inefficiencies, delivering a prioritized remediation plan at the close of every engagement.
Monitoring with Managed Grafana
Operational visibility starts with the right metrics in the right hands. Ksolves instruments Prometheus data from Flink, Spark, and Dataflow into custom Grafana dashboards built around your pipeline KPIs. Alerting rules for lag, watermark delay, and worker failures are connected to PagerDuty, OpsGenie, and Slack.
Security and Data Governance
Enterprise pipelines need security built in at every layer, not bolted on after. Ksolves secures your infrastructure with IAM-native runner authorization, Secret Manager credential injection, TLS-encrypted I/O connectors, and Cloud DLP PII masking transforms, meeting HIPAA, GDPR, and SOC 2 Type II requirements across every deployment.
Portability
One codebase runs on Dataflow, Flink, or Spark without rewrites. No vendor lock-in, no migration overhead.
Cost
Autoscaling, combiner lifting, and transform fusion significantly reduce infrastructure spend. Palo Alto Networks cut processing costs by 60% running Apache Beam on self-managed Flink.
Unified API
One SDK handles bounded and unbounded data with consistent windowing and triggering. No separate batch and streaming codebases to maintain.
Reliability
Exactly-once is available on select runners such as Dataflow. At-least-once is supported across all runners. Match consistency to your workload needs.
Security
IAM authorization, Secret Manager injection, DLP PII masking, and TLS connectors are built in. GDPR, HIPAA, and SOC 2 Type II ready.
Cloud-native
Run on managed Dataflow or self-managed Flink and Spark. Kubernetes-native configs support hybrid and on-premises deployments.
Ecosystem
Pre-built connectors for Kafka, BigQuery, Pub/Sub, Iceberg, Parquet, Cassandra, and JDBC. No custom source or sink logic needed.
12+
Years of IT expertise
Optimized Pipeline Performance
24×7
Dedicated support
Trusted by Global Enterprises
SLA-Based Service Delivery
ISO 27001, SOC 2, GDPR Compliant
Global Delivery Presence
Streaming & Batch Pipeline Expertise
Custom Cross-Runner Solutions
Seamless Runner & SDK Migrations
We deliver competitive Apache Beam data solutions across mission-critical industry verticals.
Healthcare
Retail & E-Commerce
Logistics and Supply Chain
Education
Financial Services
Manufacturing
Public Sector
Media and Entertainment
IT Industry
Telecom
What is Apache Beam and how is it different from Spark or Flink?
Apache Beam is a unified programming model, not an execution engine. Pipelines are written once and run on any compatible runner including Dataflow, Flink, or Spark. Spark and Flink are runners with proprietary APIs. Beam sits above them, giving your team full runner portability without rewriting pipeline logic.
What are the core components of an Apache Beam pipeline?
Four core abstractions: a Pipeline defines the full job. A PCollection is a distributed dataset, bounded for batch or unbounded for streaming. A PTransform is a processing step such as ParDo, GroupByKey, or Combine. A Pipeline Runner executes the graph. Windowing and Triggers control how streaming data is grouped and emitted.
Which runners does Ksolves recommend for production Apache Beam deployments?
Google Cloud Dataflow for fully managed autoscaling deployments. Apache Flink for self-managed low-latency streaming on Kubernetes. Apache Spark for large-scale batch workloads on existing infrastructure. The DirectRunner is for local testing only. Apache Samza and Twister2 are deprecated and not recommended for new deployments.
Can Ksolves migrate our existing Spark or Flink jobs to Apache Beam?
Yes. Ksolves maps existing transformation logic to Beam SDK constructs, replaces native I/O with Beam connectors, and validates output parity through before-and-after benchmarking. Staged cutover protects downstream consumers. Every migration includes a documented rollback plan.
