Bokeh is an interactive web visualization framework for Python, in the spirit of D3 but designed for non-Javascript programmers, and architected to be driven by server-side data and object model changes. Learn more about it and play with online demos at http://bokeh.pydata.org.
These slides are from a talk at San Francisco Python Meetup on September 10, 2014
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
The past few years have seen an enormous growth in the popularity of graph databases, but what exactly is a graph database and how can I use one to gain deeper insights from my data?
In this session we will introduce JanusGraph, a highly scalable, transactional graph database with flexible backend storage options such as Apache HBase, Apache Cassandra, and Oracle Berkeley DB. We will begin with a brief introduction to graph databases and data models, common use cases, and the benefits of a relationship centric approach to analytics. We will follow with a more technical dive into the features and deployment options of JanusGraph, including accessing the graph with the Apache Tinkerpop API stack, manipulating it with the Blueprints API, and querying the graph with the Gremlin query language. Finally, we will look at how JanusGraph integrates with other technologies like Apache Spark as part of an overall analytics architecture.
Deep learning has come a long way over the past few years, with advances in cloud computing, frameworks, and open source tooling, working with images has gotten simpler over time. Delta Lake has been amazing at creating a tabular structured transactional layer on object storage, but what about images? Would you like to know how to gain a 45x improvement in your image processing pipeline? Join Jason and Rohit to find out how!
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
The past few years have seen an enormous growth in the popularity of graph databases, but what exactly is a graph database and how can I use one to gain deeper insights from my data?
In this session we will introduce JanusGraph, a highly scalable, transactional graph database with flexible backend storage options such as Apache HBase, Apache Cassandra, and Oracle Berkeley DB. We will begin with a brief introduction to graph databases and data models, common use cases, and the benefits of a relationship centric approach to analytics. We will follow with a more technical dive into the features and deployment options of JanusGraph, including accessing the graph with the Apache Tinkerpop API stack, manipulating it with the Blueprints API, and querying the graph with the Gremlin query language. Finally, we will look at how JanusGraph integrates with other technologies like Apache Spark as part of an overall analytics architecture.
Deep learning has come a long way over the past few years, with advances in cloud computing, frameworks, and open source tooling, working with images has gotten simpler over time. Delta Lake has been amazing at creating a tabular structured transactional layer on object storage, but what about images? Would you like to know how to gain a 45x improvement in your image processing pipeline? Join Jason and Rohit to find out how!
Modern machine learning systems may be very complex and may fall into many pitfalls. It's very easy to unintendedly introduce technical debt into such a complex structure. One of the approaches solving some of anti-patterns is a feature store. Feature store is a missing piece filling a gap between raw data and machine learning models. Not only it will help you to handle technical debt, but even more importantly speeds up time to develop new model.
Accelerating Data Ingestion with Databricks AutoloaderDatabricks
Tracking which incoming files have been processed has always required thought and design when implementing an ETL framework. The Autoloader feature of Databricks looks to simplify this, taking away the pain of file watching and queue management. However, there can also be a lot of nuance and complexity in setting up Autoloader and managing the process of ingesting data using it. After implementing an automated data loading process in a major US CPMG, Simon has some lessons to share from the experience.
This session will run through the initial setup and configuration of Autoloader in a Microsoft Azure environment, looking at the components used and what is created behind the scenes. We’ll then look at some of the limitations of the feature, before walking through the process of overcoming these limitations. We will build out a practical example that tackles evolving schemas, applying transformations to your stream, extracting telemetry from the process and finally, how to merge the incoming data into a Delta table.
After this session you will be better equipped to use Autoloader in a data ingestion platform, simplifying your production workloads and accelerating the time to realise value in your data!
The columnar roadmap: Apache Parquet and Apache ArrowDataWorks Summit
The Hadoop ecosystem has standardized on columnar formats—Apache Parquet for on-disk storage and Apache Arrow for in-memory. With this trend, deep integration with columnar formats is a key differentiator for big data technologies. Vertical integration from storage to execution greatly improves the latency of accessing data by pushing projections and filters to the storage layer, reducing time spent in IO reading from disk, as well as CPU time spent decompressing and decoding. Standards like Arrow and Parquet make this integration even more valuable as data can now cross system boundaries without incurring costly translation. Cross-system programming using languages such as Spark, Python, or SQL can becomes as fast as native internal performance.
In this talk we’ll explain how Parquet is improving at the storage level, with metadata and statistics that will facilitate more optimizations in query engines in the future. We’ll detail how the new vectorized reader from Parquet to Arrow enables much faster reads by removing abstractions as well as several future improvements. We will also discuss how standard Arrow-based APIs pave the way to breaking the silos of big data. One example is Arrow-based universal function libraries that can be written in any language (Java, Scala, C++, Python, R, ...) and will be usable in any big data system (Spark, Impala, Presto, Drill). Another is a standard data access API with projection and predicate push downs, which will greatly simplify data access optimizations across the board.
Speaker
Julien Le Dem, Principal Engineer, WeWork
Building a data lake is a daunting task. The promise of a virtual data lake is to provide the advantages of a data lake without consolidating all data into a single repository. With Apache Arrow and Dremio, companies can, for the first time, build virtual data lakes that provide full access to data no matter where it is stored and no matter what size it is.
Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit
If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
Optimizing Delta/Parquet Data Lakes for Apache SparkDatabricks
This talk outlines data lake design patterns that can yield massive performance gains for all downstream consumers. We will talk about how to optimize Parquet data lakes and the awesome additional features provided by Databricks Delta. * Optimal file sizes in a data lake * File compaction to fix the small file problem * Why Spark hates globbing S3 files * Partitioning data lakes with partitionBy * Parquet predicate pushdown filtering * Limitations of Parquet data lakes (files aren't mutable!) * Mutating Delta lakes * Data skipping with Delta ZORDER indexes
Speaker: Matthew Powers
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Enterprise-manufacturing systems integration requires a methodology to maintain overall life cycle of production system
PERA can be a possible solution
Tools and standardized data interchange formats are required to support the use of the methodology
ISA-95 & B2MML are suitable implementations
Using LLVM to accelerate processing of data in Apache ArrowDataWorks Summit
Most query engines follow an interpreter-based approach where a SQL query is translated into a tree of relational algebra operations then fed through a conventional tuple-based iterator model to execute the query. We will explore the overhead associated with this approach and how the performance of query execution on columnar data can be improved using run-time code generation via LLVM.
Generally speaking, the best case for optimal query execution performance is a hand-written query plan that does exactly what is needed by the query for the exact same data types and format. Vectorized query processing models amortize the cost of function calls. However, research has shown that hand-written code for a given query plan has the potential to outperform the optimizations associated with a vectorized query processing model.
Over the last decade, the LLVM compiler framework has seen significant development. Furthermore, the database community has realized the potential of LLVM to boost query performance by implementing JIT query compilation frameworks. With LLVM, a SQL query is translated into a portable intermediary representation (IR) which is subsequently converted into machine code for the desired target architecture.
Dremio is built on top of Apache Arrow’s in-memory columnar vector format. The in-memory vectors map directly to the vector type in LLVM and that makes our job easier when writing the query processing algorithms in LLVM. We will talk about how Dremio implemented query processing logic in LLVM for some operators like FILTER and PROJECT. We will also discuss the performance benefits of LLVM-based vectorized query execution over other methods.
Speaker
Siddharth Teotia, Dremio, Software Engineer
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
Building Open Data Lakes on AWS with Debezium and Apache HudiGary Stafford
Build a simple open data lake on AWS using a combination of open-source software (OSS), including Red Hat’s Debezium, Apache Kafka, and Kafka Connect for change data capture (CDC), and Apache Hive, Apache Spark, Apache Hudi, and Hudi’s DeltaStreamer for managing our data lake. We will use fully-managed AWS services to host the open data lake components, including Amazon RDS, Amazon MKS, Amazon EKS, and EMR.
Link to the blog post and video: https://garystafford.medium.com/building-open-data-lakes-with-debezium-and-apache-hudi-c3370d3f86fb
Modern machine learning systems may be very complex and may fall into many pitfalls. It's very easy to unintendedly introduce technical debt into such a complex structure. One of the approaches solving some of anti-patterns is a feature store. Feature store is a missing piece filling a gap between raw data and machine learning models. Not only it will help you to handle technical debt, but even more importantly speeds up time to develop new model.
Accelerating Data Ingestion with Databricks AutoloaderDatabricks
Tracking which incoming files have been processed has always required thought and design when implementing an ETL framework. The Autoloader feature of Databricks looks to simplify this, taking away the pain of file watching and queue management. However, there can also be a lot of nuance and complexity in setting up Autoloader and managing the process of ingesting data using it. After implementing an automated data loading process in a major US CPMG, Simon has some lessons to share from the experience.
This session will run through the initial setup and configuration of Autoloader in a Microsoft Azure environment, looking at the components used and what is created behind the scenes. We’ll then look at some of the limitations of the feature, before walking through the process of overcoming these limitations. We will build out a practical example that tackles evolving schemas, applying transformations to your stream, extracting telemetry from the process and finally, how to merge the incoming data into a Delta table.
After this session you will be better equipped to use Autoloader in a data ingestion platform, simplifying your production workloads and accelerating the time to realise value in your data!
The columnar roadmap: Apache Parquet and Apache ArrowDataWorks Summit
The Hadoop ecosystem has standardized on columnar formats—Apache Parquet for on-disk storage and Apache Arrow for in-memory. With this trend, deep integration with columnar formats is a key differentiator for big data technologies. Vertical integration from storage to execution greatly improves the latency of accessing data by pushing projections and filters to the storage layer, reducing time spent in IO reading from disk, as well as CPU time spent decompressing and decoding. Standards like Arrow and Parquet make this integration even more valuable as data can now cross system boundaries without incurring costly translation. Cross-system programming using languages such as Spark, Python, or SQL can becomes as fast as native internal performance.
In this talk we’ll explain how Parquet is improving at the storage level, with metadata and statistics that will facilitate more optimizations in query engines in the future. We’ll detail how the new vectorized reader from Parquet to Arrow enables much faster reads by removing abstractions as well as several future improvements. We will also discuss how standard Arrow-based APIs pave the way to breaking the silos of big data. One example is Arrow-based universal function libraries that can be written in any language (Java, Scala, C++, Python, R, ...) and will be usable in any big data system (Spark, Impala, Presto, Drill). Another is a standard data access API with projection and predicate push downs, which will greatly simplify data access optimizations across the board.
Speaker
Julien Le Dem, Principal Engineer, WeWork
Building a data lake is a daunting task. The promise of a virtual data lake is to provide the advantages of a data lake without consolidating all data into a single repository. With Apache Arrow and Dremio, companies can, for the first time, build virtual data lakes that provide full access to data no matter where it is stored and no matter what size it is.
Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit
If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
Optimizing Delta/Parquet Data Lakes for Apache SparkDatabricks
This talk outlines data lake design patterns that can yield massive performance gains for all downstream consumers. We will talk about how to optimize Parquet data lakes and the awesome additional features provided by Databricks Delta. * Optimal file sizes in a data lake * File compaction to fix the small file problem * Why Spark hates globbing S3 files * Partitioning data lakes with partitionBy * Parquet predicate pushdown filtering * Limitations of Parquet data lakes (files aren't mutable!) * Mutating Delta lakes * Data skipping with Delta ZORDER indexes
Speaker: Matthew Powers
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Enterprise-manufacturing systems integration requires a methodology to maintain overall life cycle of production system
PERA can be a possible solution
Tools and standardized data interchange formats are required to support the use of the methodology
ISA-95 & B2MML are suitable implementations
Using LLVM to accelerate processing of data in Apache ArrowDataWorks Summit
Most query engines follow an interpreter-based approach where a SQL query is translated into a tree of relational algebra operations then fed through a conventional tuple-based iterator model to execute the query. We will explore the overhead associated with this approach and how the performance of query execution on columnar data can be improved using run-time code generation via LLVM.
Generally speaking, the best case for optimal query execution performance is a hand-written query plan that does exactly what is needed by the query for the exact same data types and format. Vectorized query processing models amortize the cost of function calls. However, research has shown that hand-written code for a given query plan has the potential to outperform the optimizations associated with a vectorized query processing model.
Over the last decade, the LLVM compiler framework has seen significant development. Furthermore, the database community has realized the potential of LLVM to boost query performance by implementing JIT query compilation frameworks. With LLVM, a SQL query is translated into a portable intermediary representation (IR) which is subsequently converted into machine code for the desired target architecture.
Dremio is built on top of Apache Arrow’s in-memory columnar vector format. The in-memory vectors map directly to the vector type in LLVM and that makes our job easier when writing the query processing algorithms in LLVM. We will talk about how Dremio implemented query processing logic in LLVM for some operators like FILTER and PROJECT. We will also discuss the performance benefits of LLVM-based vectorized query execution over other methods.
Speaker
Siddharth Teotia, Dremio, Software Engineer
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
Building Open Data Lakes on AWS with Debezium and Apache HudiGary Stafford
Build a simple open data lake on AWS using a combination of open-source software (OSS), including Red Hat’s Debezium, Apache Kafka, and Kafka Connect for change data capture (CDC), and Apache Hive, Apache Spark, Apache Hudi, and Hudi’s DeltaStreamer for managing our data lake. We will use fully-managed AWS services to host the open data lake components, including Amazon RDS, Amazon MKS, Amazon EKS, and EMR.
Link to the blog post and video: https://garystafford.medium.com/building-open-data-lakes-with-debezium-and-apache-hudi-c3370d3f86fb
ELK Stack - Turn boring logfiles into sexy dashboardGeorg Sorst
Die Präsentation zeigt, wie mit dem ELK-Stack (Elasticsearch, Logstash, Kibana) Logs von Applikationen zentralisiert verwaltet und ausgewertet werden können.
Scaling Saved Searches at eBay KleinanzeigenAndre Charton
Ebay Kleinanzeigen is one of the most visited sites in Germany and still grows at an amazing speed. Currently, we have about 19 million ads and over 18 million unique visitors each month. One of our most popular features are saved searches: When on a search result page, users can register for push notifications in case of new matching ads being posted. Introduced in summer last year, we are now close to 5 million saved searches in our database, with the number steadily growing.
Использование Elasticsearch для организации поиска по сайтуOlga Lavrentieva
Дмитрий Жлобо, Ruby and Rails Developer в Twinslash
«Использование Elasticsearch для организации поиска по сайту»
Организация качественного поиска на сайте – сложная и нетривиальная задача. В своем докладе Дмитрий расскажет о том, как ее решить с помощью Elasticsearch.
Будет рассмотрено, как Elasticsearch работает с текстом или другими данными: от анализа и индексации документов до поиска и агрегации. По шагам и на примерах будет показано, как настроить поиск, учитывающий, например, морфологию и фонетику русского языка. Также Дмитрий расскажет, как все это использовать в приложениях на Ruby, как организовать добавление документов в индекс и др.
Elasticsearch sur Azure : Make sense of your (BIG) data !Microsoft
Sous licence Apache2, elasticsearch est un moteur de recherche puissant, distribué et scalable. Il fournit également des agrégations en temps réel en fonction de vos besoins. Couplé à Kibana, dashboard générique et hautement personnalisable, il vous permet de donner immédiatement du sens à vos données. En forte progression au niveau de son adhésion par les entreprises et les sites publics, découvrez ce que sont elasticsearch et Kibana et à quel point il est simple de les déployer facilement sur la plate-forme Windows Azure. Thomas et David illustreront à l'aide de cas clients les bénéfices obtenus à travers ces solutions.
Speakers : Thomas Conté (Microsoft), David Pilato (Elasticsearch)
Collabnix Community conduct webinar on regular basis. Swapnasagar Pradhan, an engineer from VISA delivered a talk on Traefik this January 11th 2020. Check this out.
Start visualizing, analyzing and exploring Instagram feeds/influencer from South Tyrol & Trentino and inquiries/bookings from Touristic Portals from South Tyrol (BigData4Tourism working group) with Elastic.
German slides for different use cases for Elasticsearch: Document Store, full text search, flexible query cache, geospatial search, logfile analytics, analytics.
Full-Text Search Explained - Philipp Krenn - Codemotion Rome 2017Codemotion
Today’s applications are expected to provide powerful full-text search. But how does that work in general and how do I implement it on my site or in my application? Actually, this is not as hard as it sounds at first. This talk covers: * How full-text search works in general and what the differences to databases are. * How the score or quality of a search result is calculated. * How to implement this with Elasticsearch. Attendees will learn how to add common search patterns to their applications without breaking a sweat.
Demo presentation given at the Semantic Web Applications and Tools for Life Science (SWAT4LS) 2014 meeting in Berlin, Dec 10, 2014. http://www.swat4ls.org/workshops/berlin2014/scientific-programme/
PyData: Past, Present Future (PyData SV 2014 Keynote)Peter Wang
From the closing keynoteLook back at the last two years of PyData, discussion about Python's role in the growing and changing data analytics landscape, and encouragement of ways to grow the community
Python's Role in the Future of Data AnalysisPeter Wang
Why is "big data" a challenge, and what roles do high-level languages like Python have to play in this space?
The video of this talk is at: https://vimeo.com/79826022
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. About Continuum Analytics
Domains
• Finance
•Geophysics
•Defense
•Advertising metrics & data analysis
• Scientific computing
Technologies
•Array/Columnar data processing
• Distributed computing, HPC
• GPU and new vector hardware
•Machine learning, predictive analytics
• Interactive Visualization
Enterprise
Python
Data Processing
Scientific
Computing
Data Analysis
Visualisation
Scalable
Computing
3. Bokeh
• Interactive visualization
• Novel graphics
• Streaming, dynamic, large data
• For the browser, with or without a server
• No need to write Javascript
4. Interactive
• Dragging & zooming, with linking
• Selections that can round-trip to server
• Resize, entirely on client side
• Flexible hover
http://bokeh.pydata.org/gallery.html
14. Matplotlib Chaco d3 mpld3 Vincent
Interactive visualization * Y * Y
Novel graphics * * Y Y
Streaming/dynamic data * Y Y
Large data * Y Y
For the browser Y Y Y Y
No need to write Javascript Y Y Y Y Y
Works with Matplotlib Y Y Y
Works with IPython notebook Y Y Y Y
16. Previous: Javascript code generation
HTML
server.py Browser
App Model
js_str = """ <d3.js>
<highchart.js>
<etc.js>
"""
plot.js.template
D3
highcharts
flot
crossfilter
etc. ...
One-shot; no MVC interaction; no data streaming
17. BokehJS
• Full-fledged dynamic, interactive plotting engine
• materializes a reactive scenegraph from JSON
• optionally push/pull state from server, using websockets
• HTML5 Canvas, backbone.js, coffeescript, AMD, plays
with JSfiddle, …
!
“We wrote JavaScript, so you don’t have to.”
24. Other languages can generate JSON...
bokeh.r!
bokeh.h
bokeh.m
bokeh.java
...
25. New Release! v0.6
• New charts in bokeh.charts: Time Series and Categorical Heatmap
• Sophisticated Hands-on Table widget
• Complete Python 3 support for bokeh-server
• Much expanded User Guide and Dev Guide
• Multiple axes and ranges now supported
• Object query interface to help with plot styling
• Blog post coming soon (tomorrow?)
https://groups.google.com/a/continuum.io/forum/#!topic/bokeh/Hm-QNV9uQOA
30. How to Help & Contribute
• Open source BSD license for everything (JS, Python, server)
• Use it and provide feedback
• Designer? Front-end dev? - Get in touch!
• Engage us to work on custom visual exploration apps &
dashboards
• Not just code integration - also provide visualization expertise
• Helps the open source efforts
@bokehplots