Data Management
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others). The overarching goal is to create a plethora of structured data on the Web that maximally help Google users consume, interact and explore information. Through those projects, we study various cutting-edge data management research issues including information extraction and integration, large scale data analysis, effective data exploration, etc., using a variety of techniques, such as information retrieval, data mining and machine learning.
A major research effort involves the management of structured data within the enterprise. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets. This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management.
Furthermore, Data Management research across Google allows us to build technologies that power Google's largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service. Some examples of such technologies include F1, the database serving our ads infrastructure; Mesa, a petabyte-scale analytic data warehousing system; and Dremel, for petabyte-scale data processing with interactive response times. Dremel is available for external customers to use as part of Google Cloud’s BigQuery.
Recent Publications
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Vortex is an exabyte scale structured storage system built for streaming and batch analytics. It supports high-throughput batch and stream ingestion. For the user, it supports both batch-oriented and stream-based processing on the ingested data.
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BigLake: BigQuery’s Evolution toward a Multi-Cloud Lakehouse
Justin Levandoski
Garrett Casto
Mingge Deng
Rushabh Desai
Thibaud Hottelier
Amir Hormati
Jeff Johnson
Dawid Kurzyniec
Prem Ramanathan
Gaurav Saxena
Vidya Shanmugam
Yuri Volobuev
SIGMOD (2024)
Preview abstract
BigQuery’s cloud-native disaggregated architecture has allowed Google Cloud to evolve the system to meet several customer needs across the analytics and AI/ML workload spectrum. A key customer requirement for BigQuery centers around the unification of data lake and enterprise data warehousing workloads. This approach combines: (1) the need for core data management primitives, e.g., security, governance, common runtime metadata, performance acceleration, ACID transactions, provided by an enterprise data warehouses coupled with (2) harnessing the flexibility of the open source format and analytics ecosystem along with new workload types such as AI/ML over unstructured data on object storage. In addition, there is a strong requirement to support BigQuery as a multi-cloud offering given cloud customers are opting for a multi-cloud footprint by default.
This paper describes BigLake, an evolution of BigQuery toward a multi-cloud lakehouse to address these customer requirements in novel ways. We describe three main innovations in this space. We first present BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg) first class citizens, providing fine-grained governance enforcement and performance acceleration over these formats to BigQuery and other open-source analytics engines. Next, we cover the design and implementation of BigLake Object tables that allow BigQuery to integrate AI/ML for inferencing and processing over unstructured data. Finally, we present Omni, a platform for deploying BigQuery on non-GCP clouds, focusing on the infrastructure and operational innovations we made to provide an enterprise lakehouse product regardless of the cloud provider hosting the data.
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Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM, Honolulu, HI, USA (2024), pp. 9
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Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data practitioners often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific, e.g., instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data practitioners often run custom analyses for each dataset, which is cumbersome and difficult, or use unsupervised methods. We present AutoHistograms, a visualization tool leveraging LLMs. AutoHistograms automatically identifies relevant entity-based features, visualizes their distributions, and allows the user to interactively query the dataset for new categories of entities. In a user study with (n=10) data practitioners, we observe that participants were able to quickly onboard to AutoHistograms, use the tool to identify actionable insights, and conceptualize a broad range of applicable use cases. We also describe a variety of usage scenarios from different types of users to highlight how this app can provide value in many different contexts. Finally, we present a quantitative evaluation of the tool. Together, this tool and user study contribute to the growing field of LLM-assisted sensemaking tools.
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Chain-of-Table: Evolves Tables in the LLM Reasoning Chain for Table Understanding
Zilong Wang
Hao Zhang
Chun-Liang Li
Jingbo Shang
ICLR (2024)
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Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.
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Discovering Datasets on the Web Scale: Challenges and Recommendations for Google Dataset Search
Daniel Russell
Stella Dugall
Harvard Data Science Review (2024)
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With the rise of open data in the last two decades, more datasets are online and more people are using them for projects and research. But how do people find datasets? We present the first user study of Google Dataset Search, a dataset-discovery tool that uses a web crawl and open ecosystem to find datasets. Google Dataset Search contains a superset of the datasets in other dataset-discovery tools—a total of 45 million datasets from 13,000 sources. We found that the tool addresses a previously identified need: a search engine for datasets across the entire web, including datasets in other tools. However, the tool introduced new challenges due to its open approach: building a mental model of the tool, making sense of heterogeneous datasets, and learning how to search for datasets. We discuss recommendations for dataset-discovery tools and open research questions.
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Building on the simplicity and power of declarative queries combined with strongly consistent transactional semantics has allowed the Spanner database to scale to many thousands of machines running an aggregate of over 2 billion queries per second on over 8 exabytes of data. This includes some of the largest applications in the world, serving well over a billion users each.
The appetite for database storage continues to grow, potentially reaching zettabyte scale (1 billion terrabytes) by 2030. However, the end of Moore and Dennard scaling mean that the cost of the infrastructure to run those databases could grow much faster than it has in the past.
In this talk I will give my perspective on the challenges to reaching zettabyte scale, and the hardware technologies and approaches most (and least) likely to be successful.
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