BigQuery Backup Strategies: Clone vs Copy

"It just works" is the most expensive phrase in Data Engineering. We recently audited our BigQuery metadata to investigate a cost spike. The assumption? Our pipelines were getting heavier. The reality? The pipelines were fine—it was the human element driving the bill. Here is what we found (Breakdown in the slides below) The "Cache" Myth: $0 was spent on cached queries. Caching is a "nice to have," not a cost strategy. Spend was driven by non-cached, ad-hoc queries where SQL changed just enough to force a full scan. The Ad-Hoc Problem: Our biggest spend wasn't our scheduled DAGs; it was Manual Queries. Exploratory analysis from UI and BI tools (Looker/Tableau) was hitting raw tables without optimization. Our 3-Step Fix: 1️⃣ Materialize Everything: Converting repetitive manual queries into scheduled summary tables. 2️⃣ Architectural Efficiency: Using BigQuery Clones (zero-copy) and enforcing partition filters to kill unnecessary scans. 3️⃣ Governance: Implementing mandatory labels (portfolio, team, dag_name). You can’t manage what you can’t measure. The Takeaway: In BigQuery, cost isn’t about query count—it’s about data volume. Reducing the scan is the only lever that matters. How do you balance "freedom to explore" with "cost governance" in your environments? #BigQuery #FinOps #DataEngineering #GoogleCloud #GCP #DataStrategy #CloudCost

BigQuery charges for how much data a query scans, not how long it runs. A fast but sloppy query can scan an entire 10 TB table and cost A LOT. A well-written query reads only the rows and columns it needs, and answers the same question for a fraction of the cost. The same workload on the same tables can look like “usage growth” when the real issue is sloppy query design. Unravel Data makes these visible and fixable by inspecting BigQuery workloads, surfacing wasteful scans, and giving clear, line-by-line recommendations for disciplined, efficient queries. #BigQuery #FinOps #DataEngineering #DataPlatform #DataActionability

A Lakehouse is not just a place where files land. And that is where the engineering work begins. In this project, I built an end-to-end data flow in Microsoft Fabric using the Wide World Importers dataset: Pipeline ingestion   PySpark notebooks   Delta tables   Bronze, Silver, and Gold layers   Analytical tables ready for BI consumption The goal was not just to move data. It was to turn raw, distributed files into a reliable analytical model. One important detail: When Spark writes data, it does not create “one clean file” like many beginners expect. It creates distributed outputs such as: part-00001   part-00002   part-00003 That behavior is expected. But business users do not consume part files. They consume trusted tables, clear metrics, and consistent definitions. So I structured the flow like this: Bronze: raw ingestion   Silver: cleansing, standardization, and Delta persistence   Gold: facts, dimensions, and business aggregations In the Gold layer, I created analytical tables such as gold_top_products by combining sales and product data to calculate: Quantity sold   Revenue   Tax   Profit For this scenario, I kept the Gold layer inside the Lakehouse. In a production environment, I would also evaluate other options depending on the use case: Warehouse   SQL analytics endpoint   Semantic model   Hybrid architecture   External platforms like Snowflake The key lesson: Data engineering is not about loading data somewhere. It is about designing trustworthy data products that can support business decisions. That is the difference between a pipeline that runs and a data solution that matters. 🚀 Notebook codes: https://lnkd.in/eG9EAuH3 My website: https://lnkd.in/e9VpcWbd #MicrosoftFabric #DataEngineering #PySpark #SparkSQL #DeltaLake

You probably don't need a separate vector database. If you're already running #PostgreSQL, you can add AI-powered semantic search with one extension: pgvector. Here's how it works in 5 steps: - Enable the extension → CREATE EXTENSION vector; - Add a VECTOR column to your existing tables - Store embeddings from any ML model (OpenAI, Hugging Face, etc.) - Query by meaning using similarity operators in plain SQL - Add an HNSW index for fast nearest-neighbor search at scale No new infrastructure. No sync jobs. No new vendor. Just SQL and vectors. The best part? You can combine vector search with everything PostgreSQL already does joins, filters, transactions, full-text search in a single query. Use cases teams are building right now: → Smart product search that understands intent, not just keywords → FAQ chatbots that match questions by meaning → Content recommendation engines Vibhor Kumar and Marc Linster wrote a great step-by-step walkthrough covering all of this from setup to production use cases. Full article here - https://lnkd.in/gBKF2APe Follow our Substack page for more such how to tutorials straight to your inbox - Data Engineering Byte

🔗 Day 31 of my 90-Day Microsoft Fabric Blog Series is LIVE! Today: Advanced Fabric Shortcuts — External Tables and Cross-Workspace Data Sharing Most Fabric tutorials show you how to create a Lakehouse and load data into it. Today I’m covering the feature that lets you skip that step entirely: OneLake Shortcuts — query any data, anywhere, without moving it. Today’s advanced guide covers: 🔍 Shortcut vs full copy: the definitive comparison table 🌐 6 Shortcut source cards: OneLake, ADLS, Blob, Amazon S3, GCS, S3-compatible 🗃️ Creating Shortcuts: portal wizard + REST API (Python code) ⚡ External Tables: querying S3/ADLS via SQL and PySpark as if the data were local 🏛 Cross-workspace hub-and-spoke: Data Platform team publishes, domain teams consume via Shortcuts ☁️ Cross-cloud reading: Amazon S3 and GCS Shortcuts with egress cost analysis 🔐 Credential management: Managed Identity vs Account Key vs SAS — when to use each ⚡ Performance: Delta format vs CSV, partition pruning, OneLake caching 🔎 Governance: lineage view, read-only access model, audit log monitoring 🏛 5 enterprise architecture patterns: Data Mesh, legacy migration, partner data, read-back, residency 🚫 10 common errors with exact fixes The insight that changes how you think about data architecture: “Shortcuts let your Fabric platform consume data from anywhere without owning it. Build once, share everywhere, never duplicate.” 👉 Full post: https://lnkd.in/gg5ef6ZG 📌 Full series Days 1–30 in the comments. Fabric users: are you using Shortcuts for cross-workspace sharing or for external cloud sources? What’s the most creative Shortcut use case you’ve seen? Drop it in the comments. #MicrosoftFabric #DataEngineering

One thing I like about BigQuery Graph is that it pushes Google BigQuery beyond tables without pretending SQL is no longer enough… we know because of Lloyd’s work. 😄 A lot of connected data problems get forced back into joins, workarounds, and application logic when what you really want to ask is simple questions, like these examples, “What is connected to what? How did it get there? What path did it take? Where are the hidden relationships?” BigQuery Graph matters because it lets teams model data as nodes and edges inside BigQuery and query it with a graph interface that aligns with ISO GQL and SQL PGQ standards. That is a bigger deal than it sounds! The interesting part is not just the graph for the sake of the graph. It is that relational and graph analysis can now live closer together. You do not have to act as if connected data is some separate world with a separate mental model, platform, and workflow. That opens up a better way to think about fraud, supply chains, account relationships, recommendations, network effects, and all the places where ordinary tables flatten reality too early. The next step for data platforms is not just storing more data. It is giving teams better ways to express the shape of reality inside the systems they already use AND SEMANTICS. 😬 #BigQuery #GoogleCloud #DataEngineering #CloudArchitecture #GraphAnalytics #AnalyticsEngineering #MachineLearning #GCP

🔥 One of the most underrated enhancements in BigQuery right now: Graph. Same table. Same data. But now you can build a graph on top of it — and query it with GQL (Graph Query Language), right inside SQL. No migration. No new storage. Zero duplication. we're already exploring how this changes the way we model complex relationships. Structures that in a relational model require cascading joins, in a graph become natural traversals. 👉 What does BigQuery Graph unlock in practice? • Path and connection analysis (e.g. who used which equipment, in which sequence) • Anomaly detection across relationship networks • Recommendation engines on data already sitting in BigQuery • All of this without leaving the GCP ecosystem The most powerful thing? You don't have to choose between relational and graph model. You get both. On the same data. This is exactly the kind of evolution that makes your existing data platform smarter — not bigger. #BigQuery #GoogleCloud #DataEngineering #GraphDatabase #GQL #AI #Technogym

There's no single right way to load data from GCS into BigQuery. Each pattern has a different cost and latency profile — and choosing wrong wastes money or delivers stale data. Pattern 1: BigQuery Load Jobs (batch, free) bq load --source_format=PARQUET my_dataset.my_table gs://bucket/data/*.parquet - Cost: FREE (load jobs don't charge for data ingested) - Latency: 1-15 minutes per job - Best for: daily/hourly batch ingestion, initial historical loads - Supports: CSV, JSON, Parquet, Avro, ORC Pattern 2: External Tables (federated queries) CREATE EXTERNAL TABLE my_dataset.ext_orders OPTIONS (format='PARQUET', uris=['gs://bucket/orders/*.parquet']); - Cost: pay per scan (same as regular BQ queries) - Latency: zero (query data in place, no loading) - Best for: ad-hoc exploration before committing to a schema, infrequently-queried raw data Pattern 3: BigQuery Storage Write API (streaming) - Cost: $0.025/GB written - Latency: seconds to availability - Best for: real-time dashboards, CDC (Change Data Capture) pipelines - Supports exactly-once semantics (new API) or at-least-once (legacy streaming inserts) Pattern 4: Dataflow → BigQuery (streaming + transformation) GCS file arrives → Dataflow picks it up via GCS notification → transforms → writes to BQ - Cost: Dataflow compute + BQ write cost - Latency: 30s-5min depending on Dataflow job startup - Best for: files that need enrichment, validation, or schema normalization before landing in BQ Enterprise decision tree: Daily batch file → Load Job Real-time events → Storage Write API or Dataflow Explore before committing → External Table Transform + load → Dataflow #GCS #BigQuery #GCP #DataEngineering #GoogleCloud #DataIngestion #DataPipelines

Most data teams are still storing analytical data in CSV files. Here's why that's silently killing your query performance. When you store data in a row-based format — CSV, JSON, traditional relational tables — every analytical query has to scan the entire dataset to find what it needs. Want to know how many premium customers you have? The system reads every column of every row. Names, addresses, probabilities, everything — just to count one field. Parquet flips this entirely. Instead of storing data row by row, it stores it column by column. That means if you only need the isPremiumCustomer column, you read only that column. Nothing else gets touched. The practical impact: → Queries run faster because they scan less data → Compression becomes dramatically more efficient — repetitive values in a column (like 0s and 1s in a boolean field) compress far better than mixed row data → The schema is embedded in the file itself — no more "what does this column mean?" debugging at 11pm → Your compute costs drop because you're processing a fraction of the data This is why columnar formats became the default in modern data platforms — Databricks, Snowflake, BigQuery, and Microsoft Fabric's OneLake all use Parquet under the hood. It's not a trend. It's the foundation of how analytical workloads are supposed to run. If you're still defaulting to CSV for analytical pipelines, it's worth asking: is the bottleneck your queries, or your storage format? #DataEngineering #ApacheParquet #MicrosoftFabric #AnalyticsEngineering #DataArchitecture

🚀 Building Scalable & Secure Report Downloads using Azure Front Door + Blob Storage Solution where users needed to download large reports (Excel/ZIP) generated from Elasticsearch data. Instead of routing everything through the backend, we designed a more scalable and efficient architecture using Azure services. 🔹 Architecture Overview 🌐 Azure Front Door for global entry, routing & security (WAF) ⚙️ Backend API (Function App) for processing 🔍 Elasticsearch for data retrieval 📦 Azure Blob Storage for storing generated reports 🔐 SAS Token for secure, time-limited access 🔹 How it works User requests report via Azure Front Door Backend fetches data from Elasticsearch Data is transformed into Excel → zipped File uploaded to Blob Storage Secure SAS URL generated User downloads file directly from Blob 💡 Why this approach? ✔️ Offloads heavy file downloads from API ✔️ Improves performance & scalability ✔️ Enhances security with time-bound SAS access ✔️ Reduces backend load significantly ⚡ Key Learnings Azure Front Door is great for routing & protection, not for business logic Always use async processing for large report generation Avoid sending large files via API—use Blob + SAS instead This pattern works really well for high-volume reporting systems and enterprise-grade applications. #Azure #AzureFrontDoor #CloudArchitecture #DotNet #Elasticsearch #BlobStorage #ScalableSystems #CloudComputing #BackendDevelopment