BigQuery Implementation, Migration & Analytics Engineering
Wolk Inc implements, migrates, and optimises Google BigQuery data warehouses: architecture design, dbt transformation layer, slot reservation strategy, cost governance, and Looker/Looker Studio integration. From legacy DWH migration to production-grade analytics engineering on GCP.
dbt
Transformation Layer Standard
Partitioned
Table Design Default
Terraform
Infrastructure as Code
BI Engine
Sub-Second Dashboard Layer
BigQuery Consulting Deliverables
BigQuery Architecture & Implementation
BigQuery project structure: dataset organisation, table partitioning (date/ingestion/range), clustering strategy, authorised views for row and column-level security, VPC Service Controls, and IAM policy design. Flat-rate vs. on-demand cost model analysis with slot reservation recommendations. Terraform-managed infrastructure for all BigQuery resources.
Data Modelling & dbt Transformation Layer
Three-layer dbt project (staging, intermediate, marts) optimised for BigQuery's distributed execution model. Incremental materialisation with merge and insert-overwrite strategies, BigQuery-specific optimisations (partition pruning, cluster-aware queries, BI Engine acceleration), and dbt test coverage on all mart models. Partitioned incremental models for large event tables.
Analytics & BI Integration
Looker Studio (Data Studio) report templates with BigQuery direct query connection and report-level caching. Looker LookML modelling for teams on Looker enterprise. Connected Sheets for analyst self-service. BigQuery BI Engine reservation for sub-second dashboard response on high-concurrency reporting workloads.
Cost Governance & Performance Optimisation
BigQuery cost analysis: query byte scan reduction via partitioning and clustering, materialised view design for repeated expensive queries, scheduled query optimisation, and reservation slot rightsizing. Budget alerts and billing export to BigQuery for self-reporting spend by project, dataset, and team. Information Schema query for ongoing performance monitoring.
Cost-Governed BigQuery. Partition-First Design.
BigQuery Consulting Questions
Should we use BigQuery on-demand pricing or flat-rate slot reservations?▾
On-demand pricing ($6.25/TB scanned) is appropriate for organisations with variable, unpredictable workloads or those early in their BigQuery adoption. Flat-rate slot reservations (baseline + autoscale) become cost-effective when monthly spend exceeds approximately $1,500–$2,000/month consistently. The break-even depends on your workload patterns: steady continuous queries favour flat-rate; sporadic large scans may still be cheaper on-demand. Wolk Inc analyses your query history and cost model to recommend the right pricing strategy.
How does Wolk Inc migrate an existing data warehouse to BigQuery?▾
The migration follows five phases: (1) source inventory — tables, views, stored procedures, and ETL job catalogue; (2) SQL dialect compatibility assessment using BigQuery Migration Service (BQMS) for automated SQL translation; (3) schema redesign for BigQuery — denormalisation, partitioning strategy, and clustering selection; (4) historical data load via BigQuery Data Transfer Service, Storage Transfer Service, or direct load jobs; (5) pipeline re-engineering and BI tool migration with result validation. The legacy system runs in parallel during a validation period before cutover.
What is BigQuery partitioning and why does it matter for cost?▾
Partitioning divides a table into segments (by date, ingestion time, or integer range) so queries that filter on the partition column only scan the relevant partitions rather than the full table. For an event table with three years of daily data, a date-partitioned table allows a query for "last 30 days" to scan 30/1095th of the total data — a 97% cost and speed improvement. Clustering within partitions further reduces scan bytes by sorting data by high-cardinality filter columns (user ID, product ID). Wolk Inc designs partition and cluster strategies before data loading.
How does BigQuery handle semi-structured or nested JSON data?▾
BigQuery natively supports nested and repeated fields via STRUCT and ARRAY types — avoiding the full denormalisation required in traditional row-oriented warehouses. JSON event data (clickstream, app events) can be loaded directly with ARRAY<STRUCT<>> schema definitions, enabling dot-notation queries and UNNEST operations. This native support eliminates the need to flatten or explode JSON into multiple join tables, preserving query simplicity and reducing storage. Wolk Inc designs schemas that leverage nested types for event data and analytics workloads.
Can BigQuery replace our data lake for analytics?▾
BigQuery can consolidate many data lake analytics use cases through BigLake (unified storage over GCS with fine-grained access control), external tables over GCS Parquet/ORC/Avro files, and Bigtable federation for operational data. For organisations with massive volumes of rarely-queried raw data, a hybrid architecture (GCS for cold storage + BigQuery external tables or BigLake for query access) is more cost-effective than loading all raw data into managed BigQuery storage. Wolk Inc designs the storage tier boundary based on query frequency and cost model.