Migration Guide: Moving Your Analytics from Data Lakes to OLAP for Faster Landing Page Insights

Stop waiting hours for landing page answers — get near-real-time insights with OLAP

If your product marketing team is still waiting for nightly jobs to finish before they can optimize a landing page, you’re losing visits, conversions, and momentum. This guide shows a practical, step-by-step migration from data lakes to an OLAP-first analytics stack (using ClickHouse as the example) so your team can run real-time reporting, fast A/B analysis, and on-the-fly personalization with sub-second query performance.

Why migrate to OLAP in 2026 (and why now)

In late 2025 and early 2026 the industry accelerated toward real-time analytics. ClickHouse’s continued growth and funding highlight a broader trend: teams want cost-efficient columnar stores that serve production analytics and personalization simultaneously. OLAP systems are optimized for fast, ad-hoc aggregations and efficient storage of high-cardinality event streams — exactly what landing page analytics require.

ClickHouse’s major funding rounds in 2025-2026 reflect the market demand for OLAP systems built for speed and scale.

Business outcomes you’ll get

• Reporting latency reduced from hours to seconds — run live dashboards and alert on dips in conversion.
• Faster experimentation cycles — analyze A/B tests in near-real-time and shorten launch windows.
• Operational personalization — serve features and eligibility from precomputed aggregates stored in OLAP.
• Lower BI costs — columnar compression and efficient storage reduce long-term query costs versus scanning raw lake files.

High-level migration plan (4 phases)

Follow a phased approach that balances speed and risk: evaluate, pilot (streaming + historic backfill), expand, and optimize.

1. Audit & goals — define KPIs, SLAs for freshness, retention, and query patterns.
2. Pilot — build a streaming pipeline for new events + backfill historical data for a subset of traffic.
3. Rollout — cut over ingestion, standardize schemas and catalog, switch reporting sources.
4. Optimize & operationalize — compression, materialized views, monitoring, and personalization feature pipelines.

Phase 1 — Audit: decide what belongs in OLAP vs data lake

Not every dataset needs to move. Use this checklist to identify candidates for OLAP:

• Event streams used by marketers and product for dashboards, funnels, or experiments.
• Aggregates that need sub-minute freshness (conversion, drop-off, revenue per landing page).
• User feature aggregates for personalization (recent visits, last CTA clicked, signup recency).
• High-query, low-row cardinality lookups that benefit from columnar scan speed (e.g., UTM performance by campaign).

Keep raw, cold data (long-term history, raw Parquet) in your data lake for compliance and archival. OLAP complements the lake: warm, query-heavy datasets live in ClickHouse; cold datasets stay in S3/Parquet.

Phase 2 — Pilot: streaming ingestion + historical backfill

Two parallel efforts: capture new events in real-time and backfill relevant historical slices into ClickHouse. Below is a practical pipeline that works reliably in production.

Streaming pipeline (recommended)

1. Client events: use a lightweight SDK or server-side events collector on your landing pages. Batch small payloads (50–200 events) to reduce requests.
2. Message bus: publish to Kafka (or Kinesis/Pulsar). Kafka remains the most common choice for low-latency, ordered event streams used by OLAP sinks.
3. Stream processor: use Flink or a managed service (e.g., Flink on K8s, Amazon MSK + Kinesis + Amazon Kinesis Data Analytics) for enrichment, validation, and deduplication. Add fields like event_time, ingestion_time, user_id (pseudonymous), and variant_id for experiments.
4. Sink to ClickHouse: use the ClickHouse Kafka engine or a connector (e.g., ClickHouse Sink for Kafka Connect) to write transformed events into MergeTree tables. Configure batching and retries to avoid data loss.

Backfill strategy (historical load)

For historical data in Parquet/ORC on S3: perform a parallel bulk load rather than streaming every past event.

1. Export partitioned Parquet files (day/hour partitions) for the date range you need.
2. Use clickhouse-local or the S3 table function to SELECT data directly from S3 and INSERT into ClickHouse in parallel workers (multiple machines each loading a subset of partitions).
3. If the lake stores CDC logs, use a CDC tool (Debezium or your ETL) to replay changes into ClickHouse while preserving ordering.
4. Validate via row counts and sample queries before switching dashboards. Use checksums or hash comparisons for data parity.

Schema design: events, wide vs narrow tables, and denormalization

Columnar OLAP favors denormalized schemas tuned for read patterns. Design tables with query patterns in mind, not 3NF transactional purity.

Event table example (recommended)

Create a compact event table as your single source of truth for landing page events — designed for fast aggregations.

CREATE TABLE landing_events (
    event_time DateTime,
    ingestion_time DateTime DEFAULT now(),
    user_id String,
    session_id String,
    page_id String,
    variant_id String,
    utm_source String,
    utm_campaign String,
    event_type String,
    event_props Nested(key String, value String),
    revenue Float64
  ) ENGINE = MergeTree()
  PARTITION BY toYYYYMM(event_time)
  ORDER BY (page_id, event_time, user_id)
  SETTINGS index_granularity = 8192;
  

Key tips:

• Partition by time (month/day) to make retention and TTL efficient.
• Order by fields you filter/aggregate on most (page_id, event_time). Good ORDER BY drastically improves query speed.
• Store small, high-cardinality attributes as Strings or in Nested types; use dictionaries or low-cardinality codecs where appropriate.

Feature/aggregate tables for personalization

OLAP excels at precomputing user-level features for personalization. Use materialized views to maintain these aggregates in near-real-time.

CREATE MATERIALIZED VIEW user_features TO user_features_table AS
  SELECT
    user_id,
    maxIf(event_time, event_type = 'visit') AS last_visit,
    countIf(event_type = 'click') AS clicks_7d,
    sumIf(revenue, event_time >= now() - INTERVAL 30 DAY) AS rev_30d
  FROM landing_events
  GROUP BY user_id;
  

Serve these features directly via HTTP APIs or export to a low-latency cache (Redis) for real-time personalization decisions.

ETL strategies: streaming vs micro-batch vs hybrid

Choose based on SLA and engineering resources.

• Streaming (Kafka + Flink + ClickHouse): best for sub-second to minute-level freshness and continuous enrichment.
• Micro-batch (Spark/EMR or Dataproc): useful for complex joins with slow lookup tables and controlled throughput (latency: 1–15 minutes).
• Hybrid: stream current events into OLAP, and run hourly batch jobs to update expensive derived tables.

ClickHouse-specific performance tips (practical settings)

• Use MergeTree variants (ReplacingMergeTree or CollapsingMergeTree) if you need deduplication or soft-deletes in ingestion.
• Adjust index_granularity — higher values increase compression; lower values make point-range queries faster.
• Choose the right codecs (ZSTD with level tuning) for columns with large string data; use ByteArray for nested props sparingly.
• Use materialized views and projections (ClickHouse projections) to pre-aggregate heavy queries and reduce CPU at query time.
• Leverage Dictionaries for small lookup tables (campaign metadata, page metadata) to avoid expensive joins in hot queries.
• Partition by time and drop old partitions via TTL to manage retention costs automatically.

Example: speed up a conversion query

Slow version (scans many rows):

SELECT
    page_id,
    countIf(event_type = 'signup') / countIf(event_type = 'visit') AS conversion
  FROM landing_events
  WHERE event_time >= today() - 7
  GROUP BY page_id;
  

Faster version using a pre-aggregated materialized view:

CREATE MATERIALIZED VIEW agg_daily TO agg_daily_table AS
  SELECT
    toDate(event_time) AS day,
    page_id,
    countIf(event_type = 'visit') AS visits,
    countIf(event_type = 'signup') AS signups
  FROM landing_events
  GROUP BY day, page_id;

  SELECT
    page_id,
    sum(signups) / sum(visits) AS conversion
  FROM agg_daily_table
  WHERE day >= today() - 7
  GROUP BY page_id;
  

Reporting & dashboarding: connect BI tools with low latency

Connect your BI tools (Superset, Metabase, Grafana, or custom dashboards) directly to ClickHouse. Best practices:

• Use read-only DB users and query rate limits.
• Precompute heavy queries and expose dashboards against aggregated tables.
• Cache results for expensive dashboards and invalidate caches on materialized view refresh.
• Build streaming alerts: trigger notifications when conversion dips below a threshold using continuous queries or lightweight external monitors.

Personalization: real-time features from OLAP

There are two patterns to serve personalization from OLAP:

1. Precompute + cache — maintain user feature aggregates in ClickHouse and synchronize to a low-latency store (Redis) with TTLs of seconds to minutes. App servers query Redis for personalization decisions.
2. Serve from OLAP directly for low volume — for small user sets or experiments, query ClickHouse directly (after aggregations) for on-demand feature computation.

Example: get feature vector for user to decide landing page variant:

SELECT
    user_id,
    last_visit,
    clicks_7d,
    rev_30d
  FROM user_features_table
  WHERE user_id = 'user_123';
  

Push the results to Redis or your personalization API and use in real-time routing logic (edge or CDN level) to serve tailored content.

Operational considerations: data quality, monitoring, and governance

• Data quality: enforce schemas at the stream processor, run daily reconciliation jobs between lake and OLAP, and set up row-count and distribution alerts.
• Monitoring: track ingestion lag, clickhouse query time, slow query logs, and disk usage. Export metrics to Prometheus/Grafana for observability.
• Governance & privacy: pseudonymize PII before writing to ClickHouse. Keep raw personal data in secured, access-controlled lake storage. Maintain retention policies to comply with GDPR/CCPA.

Backfill & migration checklist (practical playbook)

1. Define timeframe and datasets for backfill (e.g., last 18 months of landing page events).
2. Snapshot schema of source data lake and map fields to ClickHouse schema. Identify missing fields to enrich during load.
3. Provision parallel workers: split by daily partitions and run multiple clickhouse-client loaders in parallel.
4. Run small-scale tests, validate counts and sample rows, and adjust types/encodings.
5. Run full backfill during low-traffic windows. Monitor resource usage to avoid cluster overload.
6. Once backfill is validated, switch downstream dashboards and BI connections to ClickHouse endpoints (use feature flags to control rollout).

Common pitfalls and how to avoid them

• Loading raw high-cardinality JSON blobs into ClickHouse without normalization — leads to poor compression and slow queries. Extract key fields and store the rest in compressed JSON if necessary.
• Using small partitions (per minute/hour) that multiply the number of files — use daily/monthly partitions aligned with retention needs.
• Not pre-aggregating heavy queries — rely on materialized views and projections to serve dashboards.
• Ignoring deduplication — when ingesting from multiple sources, use ReplacingMergeTree or dedupe in the stream processor.

2026 trends to watch and future-proofing your stack

• OLAP systems are increasingly used for both analytics and feature stores — plan your schema to support both use cases.
• Cloud-managed OLAP offerings simplify ops but read their SLAs — hybrid deployments (cloud + self-managed) remain common for performance-sensitive workloads.
• Privacy-first analytics: expect more in-cluster privacy features (masking, tokenization) and tighter integrations with consent frameworks.
• Streaming-first experimentation: more experiment platforms will connect directly to OLAP stores for instant experiment telemetry and stopping rules.

Example migration timeline for a marketing team (8 weeks)

1. Week 1: Audit, KPIs, choose pilot pages and events, provision ClickHouse cluster or managed instance.
2. Weeks 2–3: Build streaming ingestion for new events, create event schema, start small-scale tests.
3. Weeks 4–5: Backfill historical data for pilot pages, validate parity, create materialized views for core metrics.
4. Week 6: Connect BI tools and run QA on dashboards, train stakeholders on new dashboards and query patterns.
5. Week 7: Expand ingestion to all landing pages, build personalization feature pipelines.
6. Week 8: Cut over dashboards, deprecate old ETL, set up routine monitoring and SLOs.

Quick reference: sample ClickHouse SQL snippets

Aggregate funnel by day and variant (fast):

SELECT
    toDate(event_time) AS day,
    variant_id,
    sum(event_type = 'visit') AS visits,
    sum(event_type = 'signup') AS signups,
    signups / visits AS conversion
  FROM landing_events
  WHERE event_time >= now() - INTERVAL 7 DAY
  GROUP BY day, variant_id
  ORDER BY day, variant_id;
  

Case study snapshot (anonymized)

One SaaS marketing team migrated a 30M events/month pipeline to ClickHouse in 6 weeks. They reduced dashboard latency from 45 minutes to under 10 seconds, improved experiment turn-around from 2 weeks to 48 hours, and increased landing page conversion by 12% after adopting live personalization using OLAP-driven features cached in Redis.

Key wins: materialized views reduced CPU by 70% on heavy dashboards; dictionary tables removed expensive joins for campaign metadata.

Next steps — a practical checklist to get started today

• Run an audit of your top 10 landing page queries and measure current latency.
• Stand up a ClickHouse dev instance and replay 1 week of events into it as a pilot.
• Implement one materialized view for conversion by variant and validate a dashboard against it.
• Build a Redis sync for a single personalization feature (e.g., clicks_7d) and A/B test delivering personalized variants.

Final recommendations

Migrating analytics from data lakes to OLAP is not a rip-and-replace — it’s an evolution. Keep the raw lake for archival and compliance, and use OLAP for the warm, interactive, and production-critical analytics that drive landing page optimization and personalization. Prioritize the first 90 days: identify high-impact queries, ship a streaming pipeline for new events, and backfill in parallel. With the right schema design, materialized views, and monitoring, you’ll turn long waits into instant insights.

Call to action

Ready to cut reporting latency and ship faster landing page experiments? Start a pilot this week: export one week of landing events to a ClickHouse dev cluster and build a materialized view for conversion by variant. If you want a checklist you can run, a template schema, and a prebuilt Kafka -> ClickHouse connector manifest, request our migration playbook and a 1-hour architectural review with our team.

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