QLIK OPEN LAKEHOUSE SERVICES
Create a scalable data foundation that combines real-time ingestion, open table formats, continuous optimization and governed access for analytics and AI.
Artha helps enterprises turn Qlik Open Lakehouse into an operating data architecture, not an isolated storage project.
AI OVERVIEW
Artha Solutions designs and implements Qlik Open Lakehouse, a fully managed Apache Iceberg-based capability within Qlik Talend Cloud. Current implementations run in the customer’s AWS environment using Amazon S3 and AWS Glue, with governed access for compatible analytics and AI engines.
Qlik Open Lakehouse is a fully managed Apache Iceberg-based capability within Qlik Talend Cloud. It supports ingestion, transformation, optimization and governance of Iceberg data in a customer’s AWS environment.
Artha designs the surrounding domain model, security, quality, observability, query access and operating ownership needed to make the capability production-ready.
Warehouse pricing assumes the data earns its keep. Seven years of immutable transaction history, raw clickstream and IoT telemetry rarely do: they are queried occasionally, but they must be retained and they dominate volume. Holding them as Iceberg tables on Amazon S3 separates that storage cost from query compute, so retention stops competing with performance for the same budget.
The usual pattern is one copy per consumer, because each engine wanted its own format. Every copy is a job to schedule, a reconciliation to explain and another version of the truth. Iceberg is read natively by Athena, Spark, Trino and Snowflake, so a governed table can serve several engines without being duplicated for each of them.
Ingestion in one tool, transformation in a second, quality checks in a third and cataloguing done by hand is where lineage breaks. Nobody can answer what changed upstream of a wrong number, so incidents are diagnosed by reading code. Consolidating movement, transformation and governance behind one control plane makes that traceable rather than archaeological.
Model training needs full history and point-in-time correctness, not the aggregated current-state view that BI runs on. Iceberg snapshots make a training set reproducible: the same query against the same snapshot id returns the same rows months later, which is what turns a model result into something you can audit.
Iceberg is not maintenance-free. Frequent small writes leave thousands of tiny data files that slow scans, expired snapshots hold storage that is no longer referenced, and failed writes leave orphan files behind. Compaction, snapshot expiry and orphan cleanup have to run on a schedule tuned to each table's write pattern, or query time and S3 spend both drift upward.
Workload inventory scored on volume, freshness need and query pattern, then the AWS foundation underneath it: VPC and subnet layout, S3 bucket and prefix structure, KMS encryption, and the IAM roles that separate the platform's write access from each consuming engine's read access. Naming and layout decisions are cheap now and expensive to revisit once tables carry history.
Per-source ingestion design rather than one default. Transaction-log CDC where the source database supports it and the target needs low latency, scheduled batch where a daily window is genuinely sufficient, and streaming from Kafka or Kinesis where events arrive continuously. Each path is specified with its landing pattern, expected file sizes and the compaction cadence that follows from them.
Domain boundaries drawn so a table has one owner, partitioning chosen from the queries that will actually run rather than from the ingestion date by habit, and Glue catalog registration with defined responsibility for schema change. Partition evolution means an early choice is recoverable, but a wrong one still costs a rewrite, so this is settled with the consuming teams up front.
Quality rules with thresholds and a named owner per data product, lineage captured from source through to consuming engine, classification and masking for regulated fields, and access policy expressed once and enforced consistently across engines. Controls run in the pipeline where they can stop bad data, not in a report that describes it afterwards.
Engine integration tested against the real query mix, monitoring on freshness, table health, file counts and failed writes, S3 and compute cost attributed back to the workload that generates it, and runbooks that name who responds to each alert. Handover is to a team that has already worked an incident, not to documentation.
Databases · SAP · Mainframes · SaaS · Kafka · Kinesis · S3
Batch · CDC · Streaming
Customer VPC · Amazon S3 · Managed lakehouse compute
Apache Iceberg · AWS Glue catalog · Optimization and maintenance
Amazon Athena · Apache Spark · Trino · Snowflake · SageMaker · Qlik Cloud Analytics
Apache Iceberg is an open table format for large analytical datasets. Rather than treating a table as whatever files happen to sit in a storage folder, Iceberg keeps a metadata layer that records exactly which files belong to the table at each point in time. That one change is what makes the rest possible.
Together these bring warehouse-like reliability to data held in open object storage. What they do not bring is table maintenance, ownership or governance, which is the work that decides whether a lakehouse stays usable in its second year.
Which workloads move and which stay. High-concurrency dashboards with second-level response expectations usually belong where they are; large historical scans, raw event data and ML feature preparation are the strong candidates. Splitting on that basis is deliberate architecture, not indecision.
What each consumer actually needs, stated as a number. Streaming ingestion costs more to run and produces more small files to compact, so it is worth it where minutes matter and wasteful where a business process runs once a day. This decision sets both the pipeline design and the operating cost.
Domain boundaries, partition keys, and who owns a schema change. Iceberg allows columns to be added and partitioning to evolve without rewriting history, which removes the fear from schema change, but it does not decide who is allowed to make one or who is told when it happens.
One classification and one set of quality rules, enforced the same way whichever engine reads the table. When Athena, Spark and Snowflake each carry their own permission model, the effective access is whatever the most permissive one allows, and nobody can state what that is.
Where spend accumulates and who watches it. S3 storage is the visible line, but request volume from wide scans, compaction compute and unexpired snapshots are usually the ones that grow quietly. Cost is attributed per workload so the team generating it can see it.
The readiness assessment produces a workload inventory, architecture scorecard, prioritized use cases and target-state diagram.
CUSTOMER EVIDENCE
Selected from Artha’s existing published case-study system. Customer anonymization is preserved.
Challenge: Inventory and logistics events took more than 24 hours to reach executive reporting, limiting hot-order visibility and increasing operational intervention.
Artha solution: Artha implemented Qlik Replicate change data capture, Qlik Compose modeling and a governed cloud analytics pipeline.
Qlik Cloud, Qlik Replicate, Qlik Compose, Snowflake, Microsoft Azure, Azure, Qlik
Challenge: A healthcare analytics provider needed to ingest and govern more than 100 TB of diverse data while improving access and processing performance.
Artha solution: Artha deployed Talend with its Dynamic Ingestion Framework across AWS and Snowflake, adding automated validation and scalable processing.
Talend, AWS, Snowflake, Tableau
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FREQUENTLY ASKED QUESTIONS
Concise answers based on current Qlik product information and Artha’s consulting approach.
Qlik Open Lakehouse is a fully managed Apache Iceberg-based capability within Qlik Talend Cloud. It helps ingest, transform, optimize and govern Iceberg tables in the customer’s AWS environment for access by compatible analytics and AI engines.
Yes. Qlik Open Lakehouse uses Apache Iceberg as its open table format. Current Qlik documentation describes deployment in the customer’s AWS environment with Amazon S3 storage and AWS Glue as the catalog.
Iceberg provides an open specification, transactional consistency, schema and partition evolution, time travel and access from compatible engines. It can reduce architectural lock-in, but teams still need deliberate catalog, governance, security and operational decisions.
Not necessarily. A lakehouse may complement or replace selected warehouse workloads depending on latency, performance, governance, skills and economics. Artha evaluates workloads individually instead of assuming one destination for every dataset.
Supported sources can be onboarded using Qlik data movement with batch, change data capture and supported streaming paths. Data is processed into query-ready Iceberg tables in the configured AWS environment.
Compatible engines documented by Qlik include Amazon Athena, Apache Spark, Trino, Snowflake and Amazon SageMaker-related workflows. Exact integration and availability should be confirmed for the customer’s Qlik subscription and region during architecture.
Artha defines data ownership, classifications, quality rules, lineage, catalog responsibilities, access policy, retention and exception processes. Governance is implemented as a horizontal operating control rather than a final reporting step.
Strong first candidates have clear business value, understood source behavior, measurable freshness or cost constraints, accountable owners and manageable governance needs. A readiness assessment ranks them against complexity, risk and architectural fit.
NEXT STEP
Prioritize the right workloads and establish AWS, catalog, governance and operating assumptions before implementation.
