Rif Kiamil
I teach and educate about Data, SQL & Google BigQuery
London, United Kingdom
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As a Google Developer Expert, I teach and educate minorities and marginalised groups on Data, SQL & Google BigQuery using sports and blockchain data.
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From #NoSQL to #NoDatawarehouse: A technically light-hearted history of enterprise database
In this talk, we will explore the concept of #NoDataWarehouse and address the confusion around data terms in the context of modern enterprise data management. We will delve into the history of data modelling and databases, discussing the different types of systems, such as OLAP and OLTP, and their relevance in today's ever-changing data landscape. Furthermore, we will examine the impact of Microsoft, Google and Snowflake. Innovations, including Google App Engine and BigQuery and the influence of MapReduce, BigTable & Dremel white paper, in shaping the world of data management.
Database & SQL Performance Tuning - What Happens to Data Types as They Enter the Shadows of the Data
📌 TL:DR - This presentation explores key encoding strategies, Offset Encoding, Dictionary Encoding, Bit-Packing, RLE, and Delta Encoding, and how data ordering influences their efficiency. You'll learn how data type selection affects query execution, encoding efficiency, and cost optimization in modern data warehouse systems.
🎯 Key Takeaways
By the end of this session, you should walk away with a clear understanding
* How your choices in data type selection can influence a data warehouse, potentially lowering costs through efficient encoding
* How do DuckDB/Parquet (open-source), Google BigQuery & Azure Synapse Dedicated Pool (VertiPaq Engine) implement this in reality?
* How your data type affects query execution by limiting operator choices or leveraging low-level CPU features like Single Instruction, Multiple Data (SIMD).
Machine Mentality: Instructions Per Cycle & Vectors, Why Databases and LLMs Care
In “Machine Mentality: Instructions Per Cycle & Vectors, Why Databases and LLMs Care,” we peel back the layers of modern compute architectures to reveal how low-level encoding and vectorized execution, driven by Instructions Per Cycle (IPC) and Single Instruction, Multiple Data, pipelines, influence both database, data warehouse and large language models (LLM).
What You’ll Learn
** Encoding Strategies & Data Ordering
Explore how Offset, Dictionary, Bit-Packing, Run-Length, and Delta encodings interact with cache lines and prefetchers—and why ordering your data can make or break performance.
** Instruction Throughput & Vectorization
Understand how SIMD‐driven operators leverage wide registers to execute multiple elements per cycle, and why maximizing IPC matters for both query engines and transformer inference.
** Tokenization
Learn how the core of every transformer block consists of a small handful of linear algebra routines, and how using SIMD makes it possible to achieve those tokens-per-second speeds.
I/O Extended 2023 Sessionize Event
Rif Kiamil
I teach and educate about Data, SQL & Google BigQuery
London, United Kingdom
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