24/7 Apache Iceberg Support
Keep Your Iceberg Tables Fast & Efficient with Ksolves
We are Open source Code Contributor
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Apache Iceberg Support Services Built to Meet the World's Strictest Data Standards
En(AI)blingTM Success for Industry Leaders
Apache Iceberg Support Packages
Whether you run a single Iceberg table or a large multi-engine lakehouse, our Apache Iceberg support plans are tailored to your operational and SLA requirements.
Standard
Advanced
Platinum
Delivering Measurable Outcomes for Iceberg-Driven Enterprises
Organizations across finance, healthcare, logistics, and media trust Ksolves for enterprise-grade Apache Iceberg support and long-term lakehouse operations.
99.99%
SLA Maintained
SLA Maintained
Ksolves holds 99.99% uptime across client environments through proactive monitoring, auto-healing pipelines, and zero-drama incident response.
40%
Lower TCO
Lower TCO
From licensing audits to compute consolidation, Ksolves cuts total cost of ownership by 40%, without cutting corners on performance or reliability.
98%
Contract Renewal Rate
Contract Renewal Rate
We take pride in saying 98% of clients come back. Not because of lock-in, but because the work speaks for itself. That’s Ksolves Promise - on time, on budget, and exactly what was promised.
30 Min
Turnaround Time
Turnaround Time
Ksolves responds and resolves in under 30 minutes, keeping production running and teams unblocked.
End-to-End Apache Iceberg Support Services for Your Complete Data Lakehouse Lifecycle
From table deployment and catalog configuration to monitoring, security, and version upgrades, Ksolves manages every stage of your Iceberg lifecycle so your engineers stay focused on analytics, not operations.
24/7 Iceberg Infrastructure Management
Your dedicated Iceberg ops team, deploying tables, managing catalogs, and keeping your lakehouse healthy around the clock.Our experienced engineers act as your dedicated Apache Iceberg operations team, managing, monitoring, and optimizing your tables and catalogs 24x7 so your data teams focus on analytics, not infrastructure issues.
- Table deployment across AWS S3, Azure ADLS, GCS, and on-premise object storage
- Hive Metastore, AWS Glue, Apache Polaris, Nessie, and REST catalog configuration for high availability
- Automated snapshot expiry, orphaned file removal, and compaction scheduling
- Table properties governance covering write.distribution-mode, format-version, and commit retry configuration
- Regular health reviews and performance summaries are delivered across every support tier
Catch Issues Before They Reach Production
We deploy and operate the complete Iceberg observability layer, detecting snapshot bloat, catalog latency, and query slowdowns before they impact analytics workloads.
- Real-time table health dashboards via Prometheus, Grafana, and Datadog
- Snapshot count and metadata file size tracking with threshold-based alerting
- Partition skew and orphaned file detection across high-frequency streaming ingestion paths
- Query scan monitoring across Spark, Flink, Trino, Hive, and Dremio
- End-to-end read and write latency visibility with performance trending across all connected engines
Compliance-Ready Iceberg Security
Defense-in-depth across access control, encryption, and audit logging for GDPR, HIPAA, PCI-DSS, and SOC 2 without impacting query performance.
- Role-based access control via Apache Ranger, AWS Lake Formation, and Unity Catalog
- Column-level and row-level security for regulated data in healthcare, finance, and government
- Encryption at rest: SSE-KMS on S3, Azure Key Vault on ADLS, CMEK on GCS
- Full audit logging for table reads, writes, schema changes, snapshot operations, and catalog access
- Schema compatibility enforcement via Schema Registry with Avro, Protobuf, and JSON Schema
Zero-Downtime Iceberg Upgrades, Every Time
Every version transition is planned, validated, and executed without downtime, including migrations from Hive-based and Parquet/ORC environments.
- Pre-upgrade metadata compatibility assessment across Spark, Flink, Trino, and Hive
- Iceberg v1 to v2 format migration with positional delete and equality delete file support
- Hive-to-Iceberg in-place migration with full schema validation and partition spec preservation
- Rolling library upgrades across all connected engines with engine-specific regression testing
- Post-upgrade query benchmarking, scan efficiency comparison, and written sign-off
Iceberg Performance Fixed at the Root
We fix performance at the storage layout, partition strategy, compaction, and query engine layers, not at the symptom.
- Partition spec redesign using hidden partitioning transforms to eliminate full table scans
- Compaction strategy selection: bin-pack for read optimization vs. sort-based rewrite for range query acceleration
- Predicate pushdown and metadata filter tuning across Spark, Trino, Flink, and Dremio
- File size optimization, balancing small file accumulation from streaming writes against compaction overhead
- Object storage layout configuration using write.object-storage.enabled, combined with S3 Intelligent-Tiering for cost-efficient retention
Iceberg Architecture That Scales With Your Query Patterns
We audit your table schema, partition evolution, catalog topology, and engine integration, then fix the layer actually limiting throughput and analytical agility.
- Schema and partition key audit mapped against real query access patterns across Spark, Trino, and Flink
- Copy-on-write vs. merge-on-read selection aligned to update frequency and read SLA requirements
- Time-travel and snapshot isolation design with point-in-time recovery via rollback_to_snapshot
- Integration architecture review covering dbt incremental models, Airflow DAGs, and Kafka Tableflow connectors
- Multi-catalog topology design for environment isolation using Nessie branches or catalog-per-environment patterns
Fast Recovery. No Repeat Incidents.
When a catalog is unreachable, a metadata file is corrupt, or a write conflict stalls your pipeline, Ksolves traces every symptom to the root cause and documents it so it never recurs.
- Emergency response to catalog failures, metadata JSON corruption, and CommitFailedException errors
- Snapshot rollback and orphaned file recovery without full re-ingestion
- Concurrent write conflict diagnosis covering OCC failures and commit retry exhaustion in Spark and Flink
- Schema evolution conflict resolution across incompatible type changes and cross-engine compatibility failures
- Written Root Cause Analysis delivered for every incident, standard across all support tiers
Through the Client's Lens
Why Ksolves is a Trusted Choice of Global Teams for Apache Iceberg Support Services?
From deployment to optimization, Ksolves delivers Apache Iceberg expertise that keeps lakehouse environments secure, efficient, and built for scale.
90%
Client Retention Rate
750+
Projects Successfully
Delivered
NSE & BSE
Publicly Listed
Company
600+
Workforce and still
growing
350+
Certifications
200+
Happy Clients
150K+
Support Hours
Completed
Industries We Help Scale with Apache Iceberg
From real-time streaming ingestion to GDPR-compliant data lakehouses, Ksolves is a trusted Apache Iceberg support partner helping industries run lakehouses with maximum performance, reliability, and governance.
Telecom
Ksolves manages real-time telecom data lakehouses, handling network telemetry ingestion, subscriber event time-travel queries, and CDR tiered storage retention across multi-region Apache Iceberg support services deployments at carrier scale.
Healthcare
With deep experience in HIPAA-compliant iceberg enterprise support, we manage patient data ingestion pipelines, HL7 and FHIR record upserts, and audit-ready snapshot queries across clinical Iceberg tables with column-level access control enforced at every layer.
E-Commerce
Having worked across e-commerce data ecosystems, we keep order, inventory, and customer behaviour Iceberg tables in real-time sync using Flink-based CDC ingestion and partition-optimized schemas across every fulfilment channel.
Fintech
Understanding what fintech lakehouses demand, we manage Apache Iceberg enterprise support environments built for transaction record ingestion, fraud detection pipelines, and regulatory reporting, where every ACID commit and schema change counts.
Entertainment
Working with entertainment platforms at scale, we support high-throughput Iceberg tables for user engagement events, content metadata, and recommendation signal feeds that scale with audience demand through optimized compaction and partition design.
Manufacturing
With hands-on manufacturing data experience, we connect shop floor sensor streams and MES systems into time-windowed Iceberg tables using hour and day hidden partition transforms, with TTL-based snapshot expiry and efficient compaction scheduling.
Retail
Understanding retail data complexity, we manage Apache Iceberg support services environments connecting POS systems, loyalty platforms, and customer data across physical and digital channels into consistent, query-ready lakehouse tables built to absorb peak write surges.
Banking and Financial Services
As a compliance-aware iceberg enterprise support partner, we support banking institutions with GDPR-erasure-capable row-level delete tables, encrypted lakehouses via SSE-KMS, and audit-ready snapshot trails for regulatory reporting across multiple jurisdictions.
Logistics and Supply Chain
With proven logistics data experience, we manage Iceberg tables covering shipment state, warehouse telemetry, and carrier event streams with time-travel query support and incremental scan optimization for real-time operational dashboards.
Technology and SaaS
Working alongside technology companies, we support iceberg implementation and long-term Apache Iceberg support services for multi-tenant event data, product analytics, and internal metrics stored across cloud-native S3, ADLS, and GCS lakehouses without disruption.
Ksolves on Apache Iceberg: Insights from Enterprise Experts
Explore the latest Apache Iceberg trends, best practices, and expert insights for building scalable and high-performing lakehouses.
Success Delivered by Ksolves
Ksolves Big Data Experts have delivered excellence for multiple clients operating across industries. Explore the case studies and experience the Ksolves Impact.
Real-Time Retail Lakehouse for 200+ Global Stores
Challenge
200+ hypermarkets generated millions of daily POS transactions, but data insights arrived 24 hours late, making pricing and inventory decisions reactive.
Solution
NiFi edge processors clean data at each store. Spark Streaming writes to Iceberg tables. Trino serves live dashboards via Superset with per-region Keycloak access control.
60s
Time-to-Insight (from 24 hours)
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Real-Time Retail Lakehouse for 200+ Global Stores
Challenge
200+ hypermarkets generated millions of daily POS transactions, but data insights arrived 24 hours late, making pricing and inventory decisions reactive.
Solution
NiFi edge processors clean data at each store. Spark Streaming writes to Iceberg tables. Trino serves live dashboards via Superset with per-region Keycloak access control.
60s
Time-to-Insight (from 24 hours)
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Frequently Asked Questions
Everything you need to know before choosing an Apache Iceberg support services partner.
Apache Iceberg support services cover the full Iceberg operational lifecycle, including table setup, catalog configuration, engine integration, 24×7 monitoring, performance tuning, security hardening, version upgrades, compaction management, and emergency incident response, all under one SLA-backed engagement.
Slow Iceberg queries are typically caused by poor partition spec design, small file accumulation from frequent streaming writes, metadata bloat from unmanaged snapshots, or missing predicate pushdown in the query engine. Ksolves resolves these at the root using partition analysis, rewriteDataFiles compaction tuning, and expireSnapshots scheduling.
Configure the rewriteDataFiles procedure with appropriate target-file-size-bytes, min-file-size-bytes, and max-concurrent-file-group-rewrites settings, then automate execution via Spark or an Airflow DAG on a defined schedule to prevent small file accumulation from streaming writes.
Copy-on-write (COW) rewrites entire data files on every update, producing clean files optimal for read-heavy workloads. Merge-on-read (MOR) writes lightweight delete files alongside existing data, making writes faster but requiring readers to merge at query time. COW suits analytics. MOR suits high-frequency CDC and upsert pipelines.
Commit failures are typically caused by optimistic concurrency conflicts between simultaneous writers exhausting commit.retry.num-retries, catalog connectivity timeouts, insufficient permissions on the S3 or ADLS metadata location, or metadata file size limits on certain catalog backends.
Iceberg supports adding, dropping, renaming, reordering, and widening column types via standard ALTER TABLE commands without rewriting data files. Iceberg uses internal column IDs rather than column names, so renaming a column never breaks existing readers or downstream engines.
Catalog choice depends on your environment. AWS Glue suits AWS-native deployments. Hive Metastore suits on-premise Hadoop environments. Project Nessie provides Git-like data versioning. Apache Polaris is the emerging open REST catalog standard. Ksolves configures all of these as part of our iceberg implementation support based on your engine, cloud provider, and multi-tenancy requirements.
Yes. Iceberg integrates natively with Apache Flink via IcebergSink for exactly-once streaming writes and with Spark Structured Streaming via the iceberg format option. Flink is preferred for high-frequency, low-latency ingestion. Spark Structured Streaming suits lower-frequency micro-batch patterns.
Time travel allows querying a table at a specific snapshot or timestamp using the VERSION AS OF or TIMESTAMP AS OF syntax in Spark and Trino. Every write creates a new snapshot retained until explicitly expired via expireSnapshots. It is used for regulatory audits, pipeline debugging, and recovering from accidental data deletion.
