Wes McKinney gave a talk at the 2015 Open Data Science Conference about data frames and the state of data frame interfaces across different languages and libraries. He discussed the challenges of collaboration between different data frame communities due to the tight coupling of user interfaces, data representations, and computation engines in current data frame implementations. McKinney predicted that over time these components would decouple and specialize, improving code sharing across languages.
Improving data interoperability in Python and RWes McKinney
Apache Arrow is a new open source project that aims to establish a common in-memory data representation that can improve interoperability across data science programming languages like Python and R. It provides a standardized columnar memory format that can reduce the CPU overhead of serialization and deserialization between systems by 70-80%. The Feather file format leverages Arrow to provide a fast, language-agnostic binary file format for data frames that enables very fast read/write speeds between Python and R. While Feather has benefits, it still requires data conversion between Arrow storage and each language's native data structures; establishing a common in-memory representation at the C/C++ level could further improve sharing of algorithms and libraries.
Memory Interoperability in Analytics and Machine LearningWes McKinney
Wes McKinney gave a talk on Apache Arrow, an open source project for memory interoperability between analytics and machine learning systems. Arrow provides efficient columnar memory structures and zero-copy sharing of data between applications. It defines common data types and schemas that can be used across programming languages. Arrow is implemented in C++ and provides language bindings for other languages like Python. It aims to improve performance for tasks like data loading, preprocessing, modeling and serving. Projects like pandas, Spark and Ray are exploring using Arrow internally for more efficient data handling.
Data Science Languages and Industry AnalyticsWes McKinney
September 19, 2015 talk at Berkeley Institute for Data Science. On how comparatively poor JSON / structured data tools pose a challenge for the data science languages (Python, R, Julia, etc.).
Enabling Python to be a Better Big Data CitizenWes McKinney
These slides are from my talk at the NYC Python Meetup at ODSC Office NYC on February 17, 2016. It discusses Python's architectural challenges to interoperate with the Hadoop ecosystem and how a new project, Apache Arrow, will help.
Practical Medium Data Analytics with Python (10 Things I Hate About pandas, P...Wes McKinney
This document discusses pandas, a popular Python library for data analysis, and its limitations. It introduces Badger, a new project from DataPad that aims to address some of pandas' shortcomings like slow performance on large datasets and lack of tight database integration. The creator describes Badger as using compressed columnar storage, immutable data structures, and C kernels to perform analytics queries much faster than pandas or databases on benchmark tests of a multi-million row dataset. He envisions Badger becoming a distributed, multicore analytics platform that can also be used for ETL jobs.
Apache Arrow -- Cross-language development platform for in-memory dataWes McKinney
Wes McKinney is the creator of Python's pandas project and a primary developer of Apache Arrow, Apache Parquet, and other open-source projects. Apache Arrow is an open-source cross-language development platform for in-memory analytics that aims to improve data science tools. It provides a shared standard for memory interoperability and computation across languages through its columnar memory format and libraries. Apache Arrow has growing adoption in data science systems and is working to expand language support and computational capabilities.
Apache Arrow: Cross-language Development Platform for In-memory DataWes McKinney
Apache Arrow is an open standard for in-memory columnar data and an analytical data processing platform. It aims to simplify system architectures, improve interoperability between systems, and enable data and algorithms to be reused across different programming languages. Arrow provides a portable in-memory data format and computational libraries to build analytical data processing systems. It is language-independent and supports data sharing and algorithm reuse between libraries and processes via shared memory with near-zero overhead.
Next-generation Python Big Data Tools, powered by Apache ArrowWes McKinney
This document discusses Apache Arrow, a new open source project that aims to standardize in-memory columnar data representations. It will enable faster data sharing and analysis across systems by avoiding costly serialization. The document outlines how Arrow focuses on CPU efficiency through cache locality, vectorized operations, and minimal overhead. It provides examples of how Arrow could improve I/O performance for Python tools interacting with big data systems and the Feather file format developed using Arrow. Language bindings for Arrow are under development for Python, R, Java and other languages.
Python Data Wrangling: Preparing for the FutureWes McKinney
The document is a slide deck for a presentation on Python data wrangling and the future of the pandas project. It discusses the growth of the Python data science community and key projects like NumPy, pandas, and scikit-learn that have contributed to pandas' popularity. It outlines some issues with the current pandas codebase and proposes a new C++-based core called libpandas for pandas 2.0 to improve performance and interoperability. Benchmark results show serialization formats like Arrow and Feather outperforming pickle and CSV for transferring data.
This document discusses Apache Arrow, an open source project that aims to standardize in-memory data representations to enable efficient data sharing across systems. It summarizes Arrow's goals of improving performance by 10-100x on many workloads through a common data layer, reducing serialization overhead. The document outlines Arrow's language bindings for Java, C++, Python, R, and Julia and efforts to integrate Arrow with systems like Spark, Drill and Impala to enable faster analytics. It encourages involvement in the Apache Arrow community.
Apache Arrow at DataEngConf Barcelona 2018Wes McKinney
Wes McKinney is a leading open source developer who created Python's pandas library and now leads the Apache Arrow project. Apache Arrow is an open standard for in-memory analytics that aims to improve data sharing and reuse across systems by defining a common columnar data format and memory layout. It allows data to be accessed and algorithms to be reused across different programming languages with near-zero data copying. Arrow is being integrated into various data systems and is working to expand its computational libraries and language support.
Apache Arrow is a new standard for in-memory columnar data processing. It is a complement to Apache Parquet and Apache ORC. In this deck we review key design goals and how Arrow works in detail.
Python for Financial Data Analysis with pandasWes McKinney
This document discusses using Python and the pandas library for financial data analysis. It provides an overview of pandas, describing it as a tool that offers rich data structures and SQL-like functionality for working with time series and cross-sectional data. The document also outlines some key advantages of Python for financial data analysis tasks, such as its simple syntax, powerful built-in data types, and large standard library.
The Future of Column-Oriented Data Processing With Apache Arrow and Apache Pa...Dremio Corporation
Essentially every successful analytical DBMS in the market today makes use of column-oriented data structures. In the Hadoop ecosystem, Apache Parquet (and Apache ORC) provide similar advantages in terms of processing and storage efficiency. Apache Arrow is the in-memory counterpart to these formats and has been been embraced by over a dozen open source projects as the de facto standard for in-memory processing. In this session the PMC Chair for Apache Arrow and the PMC Chair for Apache Parquet discuss the future of column-oriented processing.
High Performance Python on Apache SparkWes McKinney
This document contains the slides from a presentation given by Wes McKinney on high performance Python on Apache Spark. The presentation discusses why Python is an important and productive language, defines what is meant by "high performance Python", and explores techniques for building fast Python software such as embracing limitations of the Python interpreter and using native data structures and compiled extensions where needed. Specific examples are provided around control flow, reading CSV files, and the importance of efficient in-memory data structures.
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
Extending Pandas using Apache Arrow and NumbaUwe Korn
With the latest release of Pandas the ability to extend it with custom dtypes was introduced. Using Apache Arrow as the in-memory storage and Numba for fast, vectorized computations on these memory regions, it is possible to extend Pandas in pure Python while achieving the same performance of the built-in types. In the talk we implement a native string type as an example.
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
Raising the Tides: Open Source Analytics for Data ScienceWes McKinney
The document discusses trends in open source analytics for data science. It notes that industry giants are opening core AI and machine learning technologies. There is also open source "disruption" in data science languages and tools. Two Sigma aims to build a collaborative data science platform through open source contributions to scale access to data and computational capabilities while enhancing productivity and collaboration. Two Sigma participates in open source to drive innovation, increase value of proprietary systems, raise awareness of challenges at scale, and attract talent. Areas of investment include Apache Arrow, Parquet, Pandas, and projects for resource management, distributed computing, and collaboration.
pandas: Powerful data analysis tools for PythonWes McKinney
Wes McKinney introduced pandas, a Python data analysis library built on NumPy. Pandas provides data structures and tools for cleaning, manipulating, and working with relational and time-series data. Key features include DataFrame for 2D data, hierarchical indexing, merging and joining data, and grouping and aggregating data. Pandas is used heavily in financial applications and has over 1500 unit tests, ensuring stability and reliability. Future goals include better time series handling and integration with other Python data science packages.
This document discusses the pandas library for Python, which provides productivity-focused tools for working with structured and time series data. It highlights key challenges in financial data like data alignment, missing data, grouping operations, and time series analysis. The author created pandas in 2008 to fill the gap between Python and domain-specific languages like R, and it has grown hugely in popularity for working with financial data in Python.
Data Tools and the Data Scientist ShortageWes McKinney
Wes McKinney discusses the shortage of data scientists and analysts. There is a shortage of 140,000-190,000 people with analytics expertise and 1.5 million managers/analysts with skills to understand and make decisions based on big data analysis in the United States alone. This shortage can be addressed through improved education, tools, and a cultural shift. New approaches and tools are needed to make data science accessible to more people and bring analytics capabilities to various industries.
Wes McKinney gave the keynote presentation at PyCon APAC 2016 in Seoul. He discussed his work on Python data analysis tools like pandas, Apache Arrow, and Feather. He also talked about open source sustainability and governance. McKinney is working on the second edition of his book Python for Data Analysis, which is scheduled for release in 2017.
Ayasdi presentation in Intel's pavilion @Strata 2015 (San Jose). Highlighting, Ayasdi's approach to analyzing large complex data, and our integration into the Hadoop ecosystem.
Spark Streaming allows processing live data streams using small batch sizes to provide low latency results. It provides a simple API to implement complex stream processing algorithms across hundreds of nodes. Spark SQL allows querying structured data using SQL or the Hive query language and integrates with Spark's batch and interactive processing. MLlib provides machine learning algorithms and pipelines to easily apply ML to large datasets. GraphX extends Spark with an API for graph-parallel computation on property graphs.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
The document discusses various benchmarks that are commonly used to evaluate Semantic Web repositories and their performance handling large amounts of RDF data. Some of the major benchmarks mentioned include the Lehigh University Benchmark (LUBM), Berlin SPARQL Benchmark (BSBM), SP2Bench, Social Network Intelligence Benchmark (SIB), and DBPedia SPARQL Benchmark. The document also provides an overview of different benchmark components and links to resources with performance results from various RDF stores and systems.
A look inside pandas design and developmentWes McKinney
This document summarizes Wes McKinney's presentation on pandas, an open source data analysis library for Python. McKinney is the lead developer of pandas and discusses its design, development, and performance advantages over other Python data analysis tools. He highlights key pandas features like the DataFrame for tabular data, fast data manipulation capabilities, and its use in financial applications. McKinney also discusses his development process, tools like IPython and Cython, and optimization techniques like profiling and algorithm exploration to ensure pandas' speed and reliability.
Ibis: Scaling Python Analytics on Hadoop and ImpalaWes McKinney
This document summarizes Wes McKinney's presentation on scaling Python analytics on Hadoop and Impala. The presentation discusses how Python has become popular for data science but does not currently scale to big data. It introduces Ibis as a project that aims to maximize Python productivity for data engineers and scientists by enabling them to solve big data problems without leaving Python. Ibis uses a composable Python DSL to generate SQL queries that can be executed using distributed SQL engines like Impala at scale.
Deep Dive : Spark Data Frames, SQL and Catalyst OptimizerSachin Aggarwal
RDD recap
Spark SQL library
Architecture of Spark SQL
Comparison with Pig and Hive Pipeline
DataFrames
Definition of a DataFrames API
DataFrames Operations
DataFrames features
Data cleansing
Diagram for logical plan container
Plan Optimization & Execution
Catalyst Analyzer
Catalyst Optimizer
Generating Physical Plan
Code Generation
Extensions
A lot of data scientists use the python library pandas for quick exploration of data. The most useful construct in pandas (based on R, I think) is the dataframe, which is a 2D array(aka matrix) with the option to “name” the columns (and rows). But pandas is not distributed, so there is a limit on the data size that can be explored.
Spark is a great map-reduce like framework that can handle very big data by using a shared nothing cluster of machines.
This work is an attempt to provide a pandas-like DSL on top of spark, so that data scientists familiar with pandas have a very gradual learning curve.
The document discusses loading data into Spark SQL and the differences between DataFrame functions and SQL. It provides examples of loading data from files, cloud storage, and directly into DataFrames from JSON and Parquet files. It also demonstrates using SQL on DataFrames after registering them as temporary views. The document outlines how to load data into RDDs and convert them to DataFrames to enable SQL querying, as well as using SQL-like functions directly in the DataFrame API.
DataFrame: Spark's new abstraction for data science by Reynold Xin of DatabricksData Con LA
Spark DataFrames provide a unified data structure and API for distributed data processing across Python, R and Scala. DataFrames allow users to manipulate distributed datasets using familiar data frame concepts from single machine tools like Pandas and dplyr. The DataFrame API is built on a logical query plan called Catalyst that is optimized for efficient execution across different languages and Spark execution engines like Tungsten.
PyData: The Next Generation | Data Day Texas 2015Cloudera, Inc.
This document discusses the past, present, and future of Python for big data analytics. It provides background on the rise of Python as a data analysis tool through projects like NumPy, pandas, and scikit-learn. However, as big data systems like Hadoop became popular, Python was not initially well-suited for problems at that scale. Recent projects like PySpark, Blaze, and Spartan aim to bring Python to big data, but challenges remain around data formats, distributed computing interfaces, and competing with Scala. The document calls for continued investment in high performance Python tools for big data to ensure its relevance in coming years.
Large-Scale Data Science on Hadoop (Intel Big Data Day)Uri Laserson
The document discusses data science workflows on Hadoop. It describes data science as involving three phases - data plumbing to ingest and transform data, exploratory analytics to investigate and analyze data, and operational analytics to build and deploy models. It provides examples of tools used for each phase including Spark, Hadoop streaming, SAS, and Python for exploratory analytics, and MLlib and Spark for operational analytics. The document also discusses lambda architectures for handling both batch and real-time analytics.
Ibis: operating the Python data ecosystem at Hadoop scale by Wes McKinneyHakka Labs
Wes McKinney gave a presentation on scaling Python analytics on Hadoop and Impala. He discussed how Python has become popular for data science but does not currently scale to large datasets. The Ibis project aims to address this by providing a composable Python API that removes the need for hand-coding SQL and allows analysts to interact with distributed SQL engines like Impala from Python. Ibis expressions are compiled to optimized SQL queries for efficient execution on large datasets.
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
Data ingest is a deceptively hard problem. In the world of big data processing, it becomes exponentially more difficult. It's not sufficient to simply land data on a system, that data must be ready for processing and analysis. The Kite SDK is a data API designed for solving the issues related to data infest and preparation. In this talk you'll see how Kite can be used for everything from simple tasks to production ready data pipelines in minutes.
This document provides an overview of NoSQL databases and their characteristics. It discusses the different eras of databases and pressures that led to the rise of NoSQL databases. It then categorizes and describes the different types of NoSQL databases, including key-value stores, document stores, column family stores, and graph databases. Specific examples like MongoDB, Cassandra, HBase, Neo4j are also outlined. The document emphasizes that the type of database chosen should depend on the problem to be solved and characteristics of the data.
Technologies for Data Analytics PlatformN Masahiro
This document discusses building a data analytics platform and summarizes various technologies that can be used. It begins by outlining reasons for analyzing data like reporting, monitoring, and exploratory analysis. It then discusses using relational databases, parallel databases, Hadoop, and columnar storage to store and process large volumes of data. Streaming technologies like Storm, Kafka, and services like Redshift, BigQuery, and Treasure Data are also summarized as options for a complete analytics platform.
This document discusses ideas and technologies for building scalable software systems and processing big data. It covers:
1. Bi-modal distribution of developers shapes architecture/design and the need for loosely/tightly coupled code.
2. Internet companies like Google and Facebook innovate at large scale using open source tools and REST architectures.
3. A REST architecture allows scalability, extensible development, and integration of tools/ideas from the internet for non-internet applications.
Simplifying Hadoop with RecordService, A Secure and Unified Data Access Path ...Cloudera, Inc.
SFHUG presentation from February 2, 2016. One of the key values of the Hadoop ecosystem is its flexibility. There is a myriad of components that make up this ecosystem, allowing Hadoop to tackle otherwise intractable problems. However, having so many components provides a significant integration, implementation, and usability burden. Features that ought to work in all the components often require sizable per-component effort to ensure correctness across the stack.
Lenni Kuff explores RecordService, a new solution to this problem that provides an API to read data from Hadoop storage managers and return them as canonical records. This eliminates the need for components to support individual file formats, handle security, perform auditing, and implement sophisticated IO scheduling and other common processing that is at the bottom of any computation.
Lenni discusses the architecture of the service and the integration work done for MapReduce and Spark. Many existing applications on those frameworks can take advantage of the service with little to no modification. Lenni demonstrates how this provides fine grain (column level and row level) security, through Sentry integration, and improves performance for existing MapReduce and Spark applications by up to 5×. Lenni concludes by discussing how this architecture can enable significant future improvements to the Hadoop ecosystem.
About the speaker: Lenni Kuff is an engineering manager at Cloudera. Before joining Cloudera, he worked at Microsoft on a number of projects including SQL Server storage engine, SQL Azure, and Hadoop on Azure. Lenni graduated from the University of Wisconsin-Madison with degrees in computer science and computer engineering.
"Analyzing Twitter Data with Hadoop - Live Demo", presented at Oracle Open World 2014. The repository for the slides is in https://github.com/cloudera/cdh-twitter-example
Data Science at Scale Using Apache Spark and Apache HadoopCloudera, Inc.
This document provides information about a data science course taught using Apache Spark and Apache Hadoop. It introduces the instructors Sean Owen and Tom White and describes what data science is and the roles of data scientists. Data scientists have skills in engineering, statistics, and business domains. The document discusses why companies need data scientists due to the growth of data and its value. It presents the tools used in data science, including Apache Spark, and how Spark can be used for both investigative and operational analytics. The course teaches a complete data science problem process through hands-on examples using tools like Hadoop, Python, R, Hive, and Spark MLlib.
Introducing Apache Kudu (Incubating) - Montreal HUG May 2016Mladen Kovacevic
The document introduces Apache Kudu (incubating), a new updatable columnar storage system for Apache Hadoop designed for fast analytics on fast and changing data. It was designed to simplify architectures that use HDFS and HBase together. Kudu aims to provide high throughput for scans, low latency for individual rows, and database-like ACID transactions. It uses a columnar format and is optimized for SSD and new storage technologies.
This document provides a summary of a presentation on Big Data and NoSQL databases. It introduces the presenters, Melissa Demsak and Don Demsak, and their backgrounds. It then discusses how data storage needs have changed with the rise of Big Data, including the problems created by large volumes of data. The presentation contrasts traditional relational database implementations with NoSQL data stores, identifying five categories of NoSQL data models: document, key-value, graph, and column family. It provides examples of databases that fall under each category. The presentation concludes with a comparison of real-world scenarios and which data storage solutions might be best suited to each scenario.
This document provides an agenda and overview of a talk on big data and data science given by Peter Wang. The key points covered include:
- An honest perspective on big data trends and challenges over time.
- Architecting systems for data exploration and analysis using tools like Continuum Analytics' Blaze and Numba libraries.
- Python's role in data science for its ecosystem of libraries and accessibility to domain experts.
Big Data Retrospective - STL Big Data IDEA Jan 2019Adam Doyle
Slides from the STL Big Data IDEA meeting from January 2019. The presenters discussed technologies to continue using, stop using, and start using in 2019.
This chapter discusses software development security. It covers topics like programming concepts, compilers and interpreters, procedural vs object-oriented languages, application development methods like waterfall vs agile models, databases, object-oriented design, assessing software vulnerabilities, and artificial intelligence techniques. The key aspects are securing the entire software development lifecycle from initial planning through operation and disposal, using secure coding practices, testing for vulnerabilities, and continually improving processes.
1. The company is building advertising management platforms to help customers make smarter decisions and reach business goals faster using real-time data. They lead the online advertising market and strive to build long-term client relationships.
2. They are hiring a Data & BI Team Leader experienced in big data technologies like Hadoop and Impala to deliver real-time insights from large data sets for tasks like fraud detection and predictive analytics.
3. They chose to use Impala for its ability to perform interactive queries directly on HDFS data without relying on MapReduce, its compatibility with HiveQL, and its support through Cloudera Manager.
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
The document discusses the future of composable data systems and provides an overview from Wes McKinney. Some key points:
- Composable data systems are designed to be modular and reusable across different components through open standards and protocols. This allows new engines to be developed more easily.
- The data landscape is shifting to an era of composability, where monolithic systems will be replaced by modular, reusable pieces.
- Areas of focus for composable systems include execution engines, query interfaces, storage protocols, and optimization.
- Projects like Apache Arrow, Ibis, Substrait, and modular engines like DuckDB, DataFusion, and Velox are moving the industry toward composability.
Solving Enterprise Data Challenges with Apache ArrowWes McKinney
This document discusses Apache Arrow, an open-source library that enables fast and efficient data interchange and processing. It summarizes the growth of Arrow and its ecosystem, including new features like the Arrow C++ query engine and Arrow Rust DataFusion. It also highlights how enterprises are using Arrow to solve challenges around data interoperability, access speed, query performance, and embeddable analytics. Case studies describe how companies like Microsoft, Google Cloud, Snowflake, and Meta leverage Arrow in their products and platforms. The presenter promotes Voltron Data's enterprise subscription and upcoming conference to support business use of Apache Arrow.
- Apache Arrow is an open-source project that provides a shared data format and library for high performance data analytics across multiple languages. It aims to unify database and data science technology stacks.
- In 2021, Ursa Labs joined forces with GPU-accelerated computing pioneers to form Voltron Data, continuing development of Apache Arrow and related projects like Arrow Flight and the Arrow R package.
- Upcoming releases of the Arrow R package will bring additional query execution capabilities like joins and window functions to improve performance and efficiency of analytics workflows in R.
Apache Arrow Flight: A New Gold Standard for Data TransportWes McKinney
This document discusses how structured data is often moved inefficiently between systems, causing waste. It introduces Apache Arrow, an open standard for in-memory data, and how Arrow can help make data movement more efficient. Systems like Snowflake and BigQuery are now using Arrow to help speed up query result fetching by enabling zero-copy data transfers and sharing file formats between query processing and storage.
ACM TechTalks : Apache Arrow and the Future of Data FramesWes McKinney
Wes McKinney gave a talk on Apache Arrow and the future of data frames. He discussed how Arrow aims to standardize columnar data formats and reduce inefficiencies in data processing. It defines an efficient binary format for transferring data between systems and programming languages. As more tools support Arrow natively, it will become more efficient to process data directly in Arrow format rather than converting between data structures. Arrow is gaining adoption in popular data tools like Spark, BigQuery, and InfluxDB to improve performance.
Apache Arrow: Present and Future @ ScaledML 2020Wes McKinney
This document discusses Apache Arrow, an open source project that provides cross-language data structures and algorithms for efficient data analytics. It summarizes the history and goals of Arrow, provides examples of how it has been adopted, and outlines ongoing development initiatives. Key points include that Arrow aims to accelerate data processing by standardizing columnar data formats and protocols, it has seen widespread adoption with over 50M installs in 2019, and active areas of work include the C++ development platform and Arrow Flight RPC framework.
PyCon Colombia 2020 Python for Data Analysis: Past, Present, and Future Wes McKinney
Wes McKinney gave a presentation on the past, present, and future of Python for data analysis. He discussed the origins and development of pandas over the past 12 years from the first open source release in 2009 to the current state. Key points included pandas receiving its first formal funding in 2019, its large community of contributors, and factors driving Python's growth for data science like its package ecosystem and education. McKinney also addressed early concerns about Python and looked to the future, highlighting projects like Apache Arrow that aim to improve performance and interoperability.
Apache Arrow: Leveling Up the Analytics StackWes McKinney
This document discusses the development of Apache Arrow, an open source in-memory data format designed for efficient analytical data processing on modern hardware. It provides a brief history of big data and analytics technologies leading to the need for Arrow. Key points about Arrow include that it aims to eliminate data serialization, enable code sharing across languages, and has over 400 contributors representing 11 programming languages. Notable subcomponents include DataFusion, Gandiva, and Plasma; and development is supported by organizations like Ursa Labs.
Apache Arrow Workshop at VLDB 2019 / BOSS SessionWes McKinney
Technical deep dive for database system developers in the Arrow columnar format, binary protocol, C++ development platform, and Arrow Flight RPC.
See demo Jupyter notebooks at https://github.com/wesm/vldb-2019-apache-arrow-workshop
Apache Arrow: Leveling Up the Data Science StackWes McKinney
Ursa Labs builds cross-language libraries like Apache Arrow for data science. Arrow provides a columnar data format and utilities for efficient serialization, IO, and querying across programming languages. Ursa Labs contributes to Arrow and funds open source developers to grow the Arrow ecosystem. Their goal is to reduce the CPU time spent on data serialization and enable faster data analysis in languages like R.
This document discusses the history and development of Python data analysis tools, including pandas. It covers Wes McKinney's work on pandas from 2008 to the present, including the motivations for making data analysis easier and more productive. It also summarizes the development of related projects like Apache Arrow for standardizing columnar data representations to improve code reuse across languages.
Shared Infrastructure for Data ScienceWes McKinney
Wes McKinney discussed the evolution of data science tools and infrastructure over the past 10 years and a vision for the next 10 years. He argued that current data science languages like Python, R, and Julia operate in "silos" with separate implementations for data storage, processing, and analytics. However, new projects like Apache Arrow aim to break down these silos by establishing shared standards for in-memory data formats and interchange that can unite the implementations across languages. Arrow provides a portable data frame format, zero-copy interchange capabilities, and potential for high performance data access and flexible computation engines. This would allow data science work to be more portable across programming languages while improving performance.
Data Science Without Borders (JupyterCon 2017)Wes McKinney
Talk about building shared, language-agnostic computational infrastructure for data science. Discusses the motivation and work that's happening in the Apache Arrow project to help (http://arrow.apache.org)
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
- How compliance can play a role in the development and deployment of AI systems
- How to model trust and transparency across products and services
- How to save time and work smarter in understanding regulatory obligations, including AI
- How to operationalize and deploy AI governance best practices in your organization
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
Generative AI technology is a fascinating field that focuses on creating comp...Nohoax Kanont
Generative AI technology is a fascinating field that focuses on creating computer models capable of generating new, original content. It leverages the power of large language models, neural networks, and machine learning to produce content that can mimic human creativity. This technology has seen a surge in innovation and adoption since the introduction of ChatGPT in 2022, leading to significant productivity benefits across various industries. With its ability to generate text, images, video, and audio, generative AI is transforming how we interact with technology and the types of tasks that can be automated.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
When looking for a good software utility to convert Outlook OST files to PST format, it is important to find one that is easy to use and has useful features. WebbyAcad OST to PST Converter Tool is a great choice because it is simple to use for anyone, whether you are tech-savvy or not. It can smoothly change your files to PST while keeping all your data safe and secure. Plus, it can handle large amounts of data and convert multiple files at once, which can save you a lot of time. It even comes with 24*7 technical support assistance and a free trial, so you can try it out before making a decision. Whether you need to recover, move, or back up your data, Webbyacad OST to PST Converter is a reliable option that gives you all the support you need to manage your Outlook data effectively.
Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
This session is focused on providing you with an introduction to task mining. We will go over different types of task mining and provide you with a real-world demo on each type of task mining in detail.
UiPath Community Day Amsterdam: Code, Collaborate, ConnectUiPathCommunity
Welcome to our third live UiPath Community Day Amsterdam! Come join us for a half-day of networking and UiPath Platform deep-dives, for devs and non-devs alike, in the middle of summer ☀.
📕 Agenda:
12:30 Welcome Coffee/Light Lunch ☕
13:00 Event opening speech
Ebert Knol, Managing Partner, Tacstone Technology
Jonathan Smith, UiPath MVP, RPA Lead, Ciphix
Cristina Vidu, Senior Marketing Manager, UiPath Community EMEA
Dion Mes, Principal Sales Engineer, UiPath
13:15 ASML: RPA as Tactical Automation
Tactical robotic process automation for solving short-term challenges, while establishing standard and re-usable interfaces that fit IT's long-term goals and objectives.
Yannic Suurmeijer, System Architect, ASML
13:30 PostNL: an insight into RPA at PostNL
Showcasing the solutions our automations have provided, the challenges we’ve faced, and the best practices we’ve developed to support our logistics operations.
Leonard Renne, RPA Developer, PostNL
13:45 Break (30')
14:15 Breakout Sessions: Round 1
Modern Document Understanding in the cloud platform: AI-driven UiPath Document Understanding
Mike Bos, Senior Automation Developer, Tacstone Technology
Process Orchestration: scale up and have your Robots work in harmony
Jon Smith, UiPath MVP, RPA Lead, Ciphix
UiPath Integration Service: connect applications, leverage prebuilt connectors, and set up customer connectors
Johans Brink, CTO, MvR digital workforce
15:00 Breakout Sessions: Round 2
Automation, and GenAI: practical use cases for value generation
Thomas Janssen, UiPath MVP, Senior Automation Developer, Automation Heroes
Human in the Loop/Action Center
Dion Mes, Principal Sales Engineer @UiPath
Improving development with coded workflows
Idris Janszen, Technical Consultant, Ilionx
15:45 End remarks
16:00 Community fun games, sharing knowledge, drinks, and bites 🍻