1. The document discusses handling small file problems in Spark ETL pipelines. It recommends keeping partition sizes between 2GB and not too small to avoid overhead problems.
2. It provides examples of transformations like aggregation, normalization, and lookup that are commonly used.
3. Pivoting data in Spark is presented as an efficient solution to transform data compared to traditional ETL tools. The example pivots data to summarize by year and quarter within minutes for billions of records.
We can leverage Delta Lake, structured streaming for write-heavy use cases. This talk will go through a use case at Intuit whereby we built MOR as an architecture to allow for a very low SLA, etc. For MOR, there are different ways to view the fresh data, so we will also go over the methods used to perfTest the various ways that we were able to arrive at the best method for the given use case.
How The Weather Company Uses Apache Spark to Serve Weather Data Fast at Low CostDatabricks
The Weather Company (TWC) collects weather data across the globe at the rate of 34 million records per hour, and the TWC History on Demand application serves that historical weather data to users via an API, averaging 600,000 requests per day. Users are increasingly consuming large quantities of historical data to train analytics models, and require efficient asynchronous APIs in addition to existing synchronous ones which use ElasticSearch. We present our architecture for asynchronous data retrieval and explain how we use Spark together with leading edge technologies to achieve an order of magnitude cost reduction while at the same time boosting performance by several orders of magnitude and tripling weather data coverage from land only to global.
Materialized Column: An Efficient Way to Optimize Queries on Nested ColumnsDatabricks
In data warehouse area, it is common to use one or more columns in complex type, such as map, and put many subfields into it. It may impact the query performance dramatically because: 1) It is a waste of IO. The whole column (in map), which may contain tens of subfields, need to be read. And Spark will traverse the whole map and get the value of the target key. 2) Vectorized read can not be exploit when nested type column is read. 3) Filter pushdown can not be utilized when nested columns is read. Over the last year, we have added a series of optimizations in Apache Spark to solve the above problems for Parquet.
This talk will break down merge in Delta Lake—what is actually happening under the hood—and then explain about how you can optimize a merge. There are even some code snippet and sample configs that will be shared.
Containerized Stream Engine to Build Modern Delta LakeDatabricks
As days goes, everything is changing, your business, your analytics platform and your data. So, Deriving the real time insights from this humongous volume of data are key for survival. This robust solution can operate you to the speed of change.
Common Strategies for Improving Performance on Your Delta LakehouseDatabricks
The Delta Architecture pattern has made the lives of data engineers much simpler, but what about improving query performance for data analysts? What are some common places to look at for tuning query performance? In this session we will cover some common techniques to apply to our delta tables to make them perform better for data analysts queries. We will look at a few examples of how you can analyze a query, and determine what to focus on to deliver better performance results.
Implementing efficient Spark application with the goal of having maximal performance often requires knowledge that goes beyond official documentation. Understanding Spark’s internal processes and features may help to design the queries in alignment with internal optimizations and thus achieve high efficiency during execution. In this talk we will focus on some internal features of Spark SQL which are not well described in official documentation with a strong emphasis on explaining these features on some basic examples while sharing some performance tips along the way.
We can leverage Delta Lake, structured streaming for write-heavy use cases. This talk will go through a use case at Intuit whereby we built MOR as an architecture to allow for a very low SLA, etc. For MOR, there are different ways to view the fresh data, so we will also go over the methods used to perfTest the various ways that we were able to arrive at the best method for the given use case.
How The Weather Company Uses Apache Spark to Serve Weather Data Fast at Low CostDatabricks
The Weather Company (TWC) collects weather data across the globe at the rate of 34 million records per hour, and the TWC History on Demand application serves that historical weather data to users via an API, averaging 600,000 requests per day. Users are increasingly consuming large quantities of historical data to train analytics models, and require efficient asynchronous APIs in addition to existing synchronous ones which use ElasticSearch. We present our architecture for asynchronous data retrieval and explain how we use Spark together with leading edge technologies to achieve an order of magnitude cost reduction while at the same time boosting performance by several orders of magnitude and tripling weather data coverage from land only to global.
Materialized Column: An Efficient Way to Optimize Queries on Nested ColumnsDatabricks
In data warehouse area, it is common to use one or more columns in complex type, such as map, and put many subfields into it. It may impact the query performance dramatically because: 1) It is a waste of IO. The whole column (in map), which may contain tens of subfields, need to be read. And Spark will traverse the whole map and get the value of the target key. 2) Vectorized read can not be exploit when nested type column is read. 3) Filter pushdown can not be utilized when nested columns is read. Over the last year, we have added a series of optimizations in Apache Spark to solve the above problems for Parquet.
This talk will break down merge in Delta Lake—what is actually happening under the hood—and then explain about how you can optimize a merge. There are even some code snippet and sample configs that will be shared.
Containerized Stream Engine to Build Modern Delta LakeDatabricks
As days goes, everything is changing, your business, your analytics platform and your data. So, Deriving the real time insights from this humongous volume of data are key for survival. This robust solution can operate you to the speed of change.
Common Strategies for Improving Performance on Your Delta LakehouseDatabricks
The Delta Architecture pattern has made the lives of data engineers much simpler, but what about improving query performance for data analysts? What are some common places to look at for tuning query performance? In this session we will cover some common techniques to apply to our delta tables to make them perform better for data analysts queries. We will look at a few examples of how you can analyze a query, and determine what to focus on to deliver better performance results.
Implementing efficient Spark application with the goal of having maximal performance often requires knowledge that goes beyond official documentation. Understanding Spark’s internal processes and features may help to design the queries in alignment with internal optimizations and thus achieve high efficiency during execution. In this talk we will focus on some internal features of Spark SQL which are not well described in official documentation with a strong emphasis on explaining these features on some basic examples while sharing some performance tips along the way.
Efficiently Building Machine Learning Models for Predictive Maintenance in th...Databricks
For each drilling site, there are thousands of different equipment operating simultaneously 24/7. For the oil & gas industry, the downtime can cost millions of dollars daily. As current standard practice, the majority of the equipment are on scheduled maintenance with standby units to reduce the downtime.
A High Performance Mutable Engagement Activity Delta LakeDatabricks
In Salesforce, our customers are using High Velocity Sales to intelligently convert leads and create new opportunities. To support it, we built the engagement activity platform to automatically capture and store user engagement activities using delta lake, which is one of the key components supporting Einstein Analytics for creating powerful reports and dashboards and Sales Cloud Einstein for training machine learning models.
To convert leads and create new opportunities requires our engagement activity delta lake to handle data mutations at scale. In this presentation, we will share the challenges and learnings from building a high performance mutable data lake using delta lake which will include:
Independent Stream Process to Support Engagement Data Life cycle
Downstream Incremental Read
High Throughput Transactions in Engagement ID Mutation
Detect Cascading ID Mutation with Graph
Data Skipping and Z-Order with I/O Pruning
High Data Consistency and Integrity
Exact Once Write Across Tables
Global Synchronization and Ordering
Operating and Supporting Delta Lake in ProductionDatabricks
Delta lake is widely adopted. There are things to be aware of when dealing with petabytes of data in Delta Lake. These smart decisions can give the best efficiency and increase the adoption of Delta. Best practices like OPTIMIZE, ZORDER have to wisely chosen. We have support stories where we successfully resolved performance issues by applying the right performance strategy. There are a set of common issues or repeated questions from our strategic customers face when using Delta and in this session we cover them and how to address them.
Everyday Probabilistic Data Structures for HumansDatabricks
Processing large amounts of data for analytical or business cases is a daily occurrence for Apache Spark users. Cost, Latency and Accuracy are 3 sides of a triangle a product owner has to trade off. When dealing with TBs of data a day and PBs of data overall, even small efficiencies have a major impact on the bottom line.
Taming the Search: A Practical Way of Enforcing GDPR and CCPA in Very Large D...Databricks
In today’s data-driven economy, companies increasingly collect more user data as their valuable assets. By contrast, users have rightfully raised the concern of how to protect their data privacy. In response, there are data privacy laws to protect user’s privacy, among which, General Data Protection Regulation (GDPR) by European Union (EU) and California Consumer Privacy Act (CCPA) are two representative laws regulating business conduct in corresponding regions
Azure Data Factory Data Wrangling with Power QueryMark Kromer
ADF has embedded Power Query in Data Factory for a code-free / data-first data wrangling experience. Use the Power Query spreadsheet-style interface in your data factory to explore and prep your data, then execute your M script at scale on ADF's Spark data flow integration runtimes.
SCALE - Stream processing and Open Data, a match made in HeavenNicolas Fränkel
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
Some countries in Europe understand the potential there's in existing data that sits behind closed fences, and passed laws to make this data available to everyone.
On the other hand, the batch processing model gets more and more obsolete: users want the information as soon as possible. While there’s a trade-off between correctness of data, and its speed of delivery, most business decisions do not rely on 100% correct data.
In this talk, I’ll explain how one can leverage the data related to public transportation in Switzerland, to display them in (near) real-time on a map.
Data Discovery at Databricks with AmundsenDatabricks
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally.
We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including:
Surface the most popular tables used within Databricks
Support fuzzy search and facet search for dataset- Surface rich metadata on datasets:
Lineage information (downstream table, upstream table, downstream jobs, downstream users)
Dataset owner
Dataset frequent users
Delta extend metadata (e.g change history)
ETL job that generates the dataset
Column stats on numeric type columns
Dashboards that use the given dataset
Use Databricks data tab to show the sample data
Surface metadata on dashboards including: create time, last update time, tables used, etc
Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
Cost-Based Optimizer Framework for Spark SQL: Spark Summit East talk by Ron H...Spark Summit
In Spark SQL’s Catalyst optimizer, many rule based optimization techniques have been implemented, but the optimizer itself can still be improved. For example, without detailed column statistics information on data distribution, it is difficult to accurately estimate the filter factor, cardinality, and thus output size of a database operator. With the inaccurate and/or misleading statistics, it often leads the optimizer to choose suboptimal query execution plans.
We added a Cost-Based Optimizer framework to Spark SQL engine. In our framework, we use Analyze Table SQL statement to collect the detailed column statistics and save them into Spark’s catalog. For the relevant columns, we collect number of distinct values, number of NULL values, maximum/minimum value, average/maximal column length, etc. Also, we save the data distribution of columns in either equal-width or equal-height histograms in order to deal with data skew effectively. Furthermore, with the number of distinct values and number of records of a table, we can determine how unique a column is although Spark SQL does not support primary key. This helps determine, for example, the output size of join operation and multi-column group-by operation.
In our framework, we compute the cardinality and output size of each database operator. With reliable statistics and derived cardinalities, we are able to make good decisions in these areas: selecting the correct build side of a hash-join operation, choosing the right join type (broadcast hash-join versus shuffled hash-join), adjusting multi-way join order, etc. In this talk, we will show Spark SQL’s new Cost-Based Optimizer framework and its performance impact on TPC-DS benchmark queries.
Change Data Feed is a new feature of Delta Lake on Databricks that is available as a public preview since DBR 8.2. This feature enables a new class of ETL workloads such as incremental table/view maintenance and change auditing that were not possible before. In short, users will now be able to query row level changes across different versions of a Delta table.
In this talk we will dive into how Change Data Feed works under the hood and how to use it with existing ETL jobs to make them more efficient and also go over some new workloads it can enable.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Diving into Delta Lake: Unpacking the Transaction LogDatabricks
The transaction log is key to understanding Delta Lake because it is the common thread that runs through many of its most important features, including ACID transactions, scalable metadata handling, time travel, and more. In this session, we’ll explore what the Delta Lake transaction log is, how it works at the file level, and how it offers an elegant solution to the problem of multiple concurrent reads and writes.
ELECTRÓNICA+RADIO+TV. Tomo IV: AMPLIFICADORES B.F. ALTAVOCES. VÁLVULAS AMPLIFICADORAS.
Lección 19: Amplificadores de sonido. Altavoces: Tipos. El primer receptor con altavoz.
Lección 20: La distorsión. Distorsión en los amplificadores de intensidad, de tensión y de potencia. Potencia de disipación de placa. Curva de máxima disipación. Montaje de un receptor con amplificador de B.F. (pentodo)
Lección 21: Los controles de tono. Grabación y reproducción de discos. Estudio de un amplificador para tocadiscos. Estudio práctico de una maleta tocadiscos.
Lección 22: Las válvulas amplificadoras: más características. La E184 como amplificador de potencia. Amplificador con dos etapas.
Esta obra perteneció a un curso a distancia durante los años 60-70 y se encuentra descatalogada.La tecnología empleada, por tanto, ha quedado obsoleta, pero la teoría permanece y está expuesta con una pedagogía excelente. Es una obra básica para los estudiantes y digna de figurar en la biblioteca de cualquier profesional de la electrónica. Febrero de 2017.
Efficiently Building Machine Learning Models for Predictive Maintenance in th...Databricks
For each drilling site, there are thousands of different equipment operating simultaneously 24/7. For the oil & gas industry, the downtime can cost millions of dollars daily. As current standard practice, the majority of the equipment are on scheduled maintenance with standby units to reduce the downtime.
A High Performance Mutable Engagement Activity Delta LakeDatabricks
In Salesforce, our customers are using High Velocity Sales to intelligently convert leads and create new opportunities. To support it, we built the engagement activity platform to automatically capture and store user engagement activities using delta lake, which is one of the key components supporting Einstein Analytics for creating powerful reports and dashboards and Sales Cloud Einstein for training machine learning models.
To convert leads and create new opportunities requires our engagement activity delta lake to handle data mutations at scale. In this presentation, we will share the challenges and learnings from building a high performance mutable data lake using delta lake which will include:
Independent Stream Process to Support Engagement Data Life cycle
Downstream Incremental Read
High Throughput Transactions in Engagement ID Mutation
Detect Cascading ID Mutation with Graph
Data Skipping and Z-Order with I/O Pruning
High Data Consistency and Integrity
Exact Once Write Across Tables
Global Synchronization and Ordering
Operating and Supporting Delta Lake in ProductionDatabricks
Delta lake is widely adopted. There are things to be aware of when dealing with petabytes of data in Delta Lake. These smart decisions can give the best efficiency and increase the adoption of Delta. Best practices like OPTIMIZE, ZORDER have to wisely chosen. We have support stories where we successfully resolved performance issues by applying the right performance strategy. There are a set of common issues or repeated questions from our strategic customers face when using Delta and in this session we cover them and how to address them.
Everyday Probabilistic Data Structures for HumansDatabricks
Processing large amounts of data for analytical or business cases is a daily occurrence for Apache Spark users. Cost, Latency and Accuracy are 3 sides of a triangle a product owner has to trade off. When dealing with TBs of data a day and PBs of data overall, even small efficiencies have a major impact on the bottom line.
Taming the Search: A Practical Way of Enforcing GDPR and CCPA in Very Large D...Databricks
In today’s data-driven economy, companies increasingly collect more user data as their valuable assets. By contrast, users have rightfully raised the concern of how to protect their data privacy. In response, there are data privacy laws to protect user’s privacy, among which, General Data Protection Regulation (GDPR) by European Union (EU) and California Consumer Privacy Act (CCPA) are two representative laws regulating business conduct in corresponding regions
Azure Data Factory Data Wrangling with Power QueryMark Kromer
ADF has embedded Power Query in Data Factory for a code-free / data-first data wrangling experience. Use the Power Query spreadsheet-style interface in your data factory to explore and prep your data, then execute your M script at scale on ADF's Spark data flow integration runtimes.
SCALE - Stream processing and Open Data, a match made in HeavenNicolas Fränkel
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
Some countries in Europe understand the potential there's in existing data that sits behind closed fences, and passed laws to make this data available to everyone.
On the other hand, the batch processing model gets more and more obsolete: users want the information as soon as possible. While there’s a trade-off between correctness of data, and its speed of delivery, most business decisions do not rely on 100% correct data.
In this talk, I’ll explain how one can leverage the data related to public transportation in Switzerland, to display them in (near) real-time on a map.
Data Discovery at Databricks with AmundsenDatabricks
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally.
We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including:
Surface the most popular tables used within Databricks
Support fuzzy search and facet search for dataset- Surface rich metadata on datasets:
Lineage information (downstream table, upstream table, downstream jobs, downstream users)
Dataset owner
Dataset frequent users
Delta extend metadata (e.g change history)
ETL job that generates the dataset
Column stats on numeric type columns
Dashboards that use the given dataset
Use Databricks data tab to show the sample data
Surface metadata on dashboards including: create time, last update time, tables used, etc
Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
Cost-Based Optimizer Framework for Spark SQL: Spark Summit East talk by Ron H...Spark Summit
In Spark SQL’s Catalyst optimizer, many rule based optimization techniques have been implemented, but the optimizer itself can still be improved. For example, without detailed column statistics information on data distribution, it is difficult to accurately estimate the filter factor, cardinality, and thus output size of a database operator. With the inaccurate and/or misleading statistics, it often leads the optimizer to choose suboptimal query execution plans.
We added a Cost-Based Optimizer framework to Spark SQL engine. In our framework, we use Analyze Table SQL statement to collect the detailed column statistics and save them into Spark’s catalog. For the relevant columns, we collect number of distinct values, number of NULL values, maximum/minimum value, average/maximal column length, etc. Also, we save the data distribution of columns in either equal-width or equal-height histograms in order to deal with data skew effectively. Furthermore, with the number of distinct values and number of records of a table, we can determine how unique a column is although Spark SQL does not support primary key. This helps determine, for example, the output size of join operation and multi-column group-by operation.
In our framework, we compute the cardinality and output size of each database operator. With reliable statistics and derived cardinalities, we are able to make good decisions in these areas: selecting the correct build side of a hash-join operation, choosing the right join type (broadcast hash-join versus shuffled hash-join), adjusting multi-way join order, etc. In this talk, we will show Spark SQL’s new Cost-Based Optimizer framework and its performance impact on TPC-DS benchmark queries.
Change Data Feed is a new feature of Delta Lake on Databricks that is available as a public preview since DBR 8.2. This feature enables a new class of ETL workloads such as incremental table/view maintenance and change auditing that were not possible before. In short, users will now be able to query row level changes across different versions of a Delta table.
In this talk we will dive into how Change Data Feed works under the hood and how to use it with existing ETL jobs to make them more efficient and also go over some new workloads it can enable.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Diving into Delta Lake: Unpacking the Transaction LogDatabricks
The transaction log is key to understanding Delta Lake because it is the common thread that runs through many of its most important features, including ACID transactions, scalable metadata handling, time travel, and more. In this session, we’ll explore what the Delta Lake transaction log is, how it works at the file level, and how it offers an elegant solution to the problem of multiple concurrent reads and writes.
ELECTRÓNICA+RADIO+TV. Tomo IV: AMPLIFICADORES B.F. ALTAVOCES. VÁLVULAS AMPLIFICADORAS.
Lección 19: Amplificadores de sonido. Altavoces: Tipos. El primer receptor con altavoz.
Lección 20: La distorsión. Distorsión en los amplificadores de intensidad, de tensión y de potencia. Potencia de disipación de placa. Curva de máxima disipación. Montaje de un receptor con amplificador de B.F. (pentodo)
Lección 21: Los controles de tono. Grabación y reproducción de discos. Estudio de un amplificador para tocadiscos. Estudio práctico de una maleta tocadiscos.
Lección 22: Las válvulas amplificadoras: más características. La E184 como amplificador de potencia. Amplificador con dos etapas.
Esta obra perteneció a un curso a distancia durante los años 60-70 y se encuentra descatalogada.La tecnología empleada, por tanto, ha quedado obsoleta, pero la teoría permanece y está expuesta con una pedagogía excelente. Es una obra básica para los estudiantes y digna de figurar en la biblioteca de cualquier profesional de la electrónica. Febrero de 2017.
ELECTRÓNICA+RADIO+TV. Tomo III: DETECTORES. OSCILADORES. AMPLIFICADORES.
Apéndice: Realizaciones Prácticas.
Lección Práctica 13: Manejo del téster. Instrucciones para la medición de tensiones continuas y alternas. Medición de intensidades continuas. Medición de resistencias.
Lección Práctica 14: Nuestro primer receptor a reacción.
Lección Práctica 15: Receptor a reacción con amplificador de intensidad. Esquema teórico y esquema práctico montaje. Operaciones a seguir.
Lección Práctica 16: Análisis de tensiones e intensidades en el circuito del receptor a reacción.
Lección Práctica 17: Esquemas prácticos y esquemas teóricos: Ventajas e inconvenientes.
Esta obra perteneció a un curso a distancia durante los años 60-70 y se encuentra descatalogada.La tecnología empleada, por tanto, ha quedado obsoleta, pero la teoría permanece y está expuesta con una pedagogía excelente. Es una obra básica para los estudiantes y digna de figurar en la biblioteca de cualquier profesional de la electrónica. Por ello me he tomado el trabajo de escanearlos y ponerlos a disposición de aquellos a los que pueda interesar. Febrero de 2017.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
Big Data Transformation Powered By Apache Spark.pptxKnoldus Inc.
Witness how Spark revolutionizes data processing. Dive into transformative functions like aggregation, array manipulation, and advanced joins, unveiling Spark as the driving force for actionable insights in the vast expanse of big data.
Big Data Transformations Powered By SparkKnoldus Inc.
Witness how Spark revolutionizes data processing. Big Data is everywhere and see how spark leverages its features to provide valuable insights to the businesses. Dive into transformative functions like aggregation, array manipulation, and advanced joins, unveiling Spark as the driving force for actionable insights in the vast expanse of big data.
Learnings Using Spark Streaming and DataFrames for Walmart Search: Spark Summ...Spark Summit
In this presentation, we are going to talk about the state of the art infrastructure we have established at Walmart Labs for the Search product using Spark Streaming and DataFrames. First, we have been able to successfully use multiple micro batch spark streaming pipelines to update and process information like product availability, pick up today etc. along with updating our product catalog information in our search index to up to 10,000 kafka events per sec in near real-time. Earlier, all the product catalog changes in the index had a 24 hour delay, using Spark Streaming we have made it possible to see these changes in near real-time. This addition has provided a great boost to the business by giving the end-costumers instant access to features likes availability of a product, store pick up, etc.
Second, we have built a scalable anomaly detection framework purely using Spark Data Frames that is being used by our data pipelines to detect abnormality in search data. Anomaly detection is an important problem not only in the search domain but also many domains such as performance monitoring, fraud detection, etc. During this, we realized that not only are Spark DataFrames able to process information faster but also are more flexible to work with. One could write hive like queries, pig like code, UDFs, UDAFs, python like code etc. all at the same place very easily and can build DataFrame template which can be used and reused by multiple teams effectively. We believe that if implemented correctly Spark Data Frames can potentially replace hive/pig in big data space and have the potential of becoming unified data language.
We conclude that Spark Streaming and Data Frames are the key to processing extremely large streams of data in real-time with ease of use.
PayPal merchant ecosystem using Apache Spark, Hive, Druid, and HBase DataWorks Summit
As one of the few closed-loop payment platforms, PayPal is uniquely positioned to provide merchants with insights aimed to identify opportunities to help grow and manage their business. PayPal processes billions of data events every day around our users, risk, payments, web behavior and identity. We are motivated to use this data to enable solutions to help our merchants maximize the number of successful transactions (checkout-conversion), better understand who their customers are and find additional opportunities to grow and attract new customers.
As part of the Merchant Data Analytics, we have built a platform that serves low latency, scalable analytics and insights by leveraging some of the established and emerging platforms to best realize returns on the many business objectives at PayPal.
Join us to learn more about how we leveraged platforms and technologies like Spark, Hive, Druid, Elastic Search and HBase to process large scale data for enabling impactful merchant solutions. We’ll share the architecture of our data pipelines, some real dashboards and the challenges involved.
Speakers
Kasiviswanathan Natarajan, Member of Technical Staff, PayPal
Deepika Khera, Senior Manager - Merchant Data Analytics, PayPal
Self-serve analytics journey at Celtra: Snowflake, Spark, and DatabricksGrega Kespret
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both.
Topics include:
- Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline
- How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution
- How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency
- How Snowflake + Spark enables production and ad hoc analytics on a single repository of data
Give you a brief overview of the product. - What is esProc SPL? And show some cases helping you to know what it uses for. Talk about why esProc works better. And overview its brief characteristics. After that, Introduce the main technical solutions which esProc is often used.
Java Developers, make the database work for you (NLJUG JFall 2010)Lucas Jellema
The general consensus among Java developers has evolved from a dogmatic strive for database independence to a much more pragmatic wish to leverage the power of the database. This session demonstrates some of the (hidden) powers of the database and how these can be utilized from Java applications using either straight JDBC or working through JPA. The Oracle database is used as example: SQL for Aggregation and Analysis, Flashback Queries for historical comparison and trends, Virtual Private Database, complex validation, PL/SQL and collections for bulk data manipulation, view and instead-of triggers for data model morphing, server push of relevant data changes, edition based redefinition for release management.
- overview of role of database in JEE architecture (and a little history on how the database is perceived through the years)
- discussion on the development of database functionality
- demonstration of some powerful database features
- description of how we leveraged these features in our JSF (RichFaces)/JPA (Hibernate) application
- demo of web application based on these features
- discussion on how to approach the database
Extending Apache Spark SQL Data Source APIs with Join Push Down with Ioana De...Databricks
When Spark applications operate on distributed data coming from disparate data sources, they often have to directly query data sources external to Spark such as backing relational databases, or data warehouses. For that, Spark provides Data Source APIs, which are a pluggable mechanism for accessing structured data through Spark SQL. Data Source APIs are tightly integrated with the Spark Optimizer. They provide optimizations such as filter push down to the external data source and column pruning. While these optimizations significantly speed up Spark query execution, depending on the data source, they only provide a subset of the functionality that can be pushed down and executed at the data source. As part of our ongoing project to provide a generic data source push down API, this presentation will show our work related to join push down. An example is star-schema join, which can be simply viewed as filters applied to the fact table. Today, Spark Optimizer recognizes star-schema joins based on heuristics and executes star-joins using efficient left-deep trees. An alternative execution proposed by this work is to push down the star-join to the external data source in order to take advantage of multi-column indexes defined on the fact tables, and other star-join optimization techniques implemented by the relational data source.
Movile Internet Movel SA: A Change of Seasons: A big move to Apache CassandraDataStax Academy
A few years ago, processing large volumes of data was an exclusive problem of big companies. Nowadays, technological advancement allows people to be connected with each other all the time, generating and consuming large amounts of data.
In the challenge to follow Movile's exponential growth and increasing volume of information, we soon realized that traditional relational database and data analysis solutions were no longer a good fit to solve new order issues. Therefore, we present Movile's 'Change Of Seasons', a use case on adopting Apache Cassandra as a solution for critical high-performance distributed systems.
Cassandra Summit 2015 - A Change of SeasonsEiti Kimura
A CHANGE OF SEASONS: A big move to Apache Cassandra!
This is an extended version of the material presented at Cassandra Summit 2015 - Santa Clara - California - USA.
In this presentation I will show you 3 moves, use cases, that constitute our Big Move to Apache Cassandra @Movile.
Walking through relational model to NoSQL solution, hybrid platforms and a staggering cost reduction and throughput increase.
What Are the Key Steps in Scraping Product Data from Amazon India.pptxProductdata Scrape
Scraping data from Nykaa, Purplle, and Zeptonow offers insights for market analysis, competitive intelligence, pricing strategies, and inventory management.
Know More:
https://www.productdatascrape.com/scraping-nykaa-purplle-and-zeptonow-data-for-beauty-and-makeup-products.php
What Are the Key Steps in Scraping Product Data from Amazon India.pdfProductdata Scrape
Scraping Product Data from Amazon India enables users to extract vital information, empowering data-driven decisions and insightful analysis for various purposes.
Know More:
https://www.productdatascrape.com/scraping-product-data-from-amazon-india-website.php
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
ETL and pivoting in spark
1. ETL, pivoting and Handling Small File Problems in Spark
Extracting, Putting several transformation and Finally Loading the summarized data into hive
is the most important part of Data Warehousing. Now we face various types of problems in
spark in terms of developing you basic Data Quality Checking. So it is always
recommendable to pass the through the Data with Custom Data Quality checking steps like:
1. Null Checking in String Field
2. Null checking in Numeric Field
3. Alfa-Numeric Characters in Numeric field
4. Data Type selection on the basis of future requirements
5. Data format conversion(Most Important)
6. Filter Data
7. Address, SSN, Telephone, Email id validation etc.
In the transformation phase Spark demands many User Defined Functions as our
requirement goes more complex
Transformation like:
1. Aggregation
2. Routing
3. Normalization
4. De-Normalization
5. Intelligent Counter
6. Lookup
Load phase is putting your temporary table into Hive or HBase or Cassandra and use any
Visualization tool to show the outcome.
Now this article looks into another aspect of Small files handling in Spark which is really
important. It is said to keep in mind that “Don’t let your partition volume too high (Greater
than 2GB and don’t even make it too small which will cause overhead problem”
Now my data source is consisting of many small files, so do look at this step:
2. Now this execution plan itself shows the beauty of this hack and efficient use of broadcast
variable in spark.
This will definitely reduce down your I/O overhead problems for and provide a better result
in terms of performance.
So the data source is something like this:
The schema goes like this:
Now this data have different null problems where we need to create custom function in
RDD level and format the data.
Another problem with this data is the date format was not same throughout the file,
somewhere it’s like dd/mm/yyyy and somewhere dd-mm-yyyy. So serious amount of Data
Quality and conversion checking was required.
val dataRDD = data.map(line =>
line.split(",")).map(line=>ScoreRecord(checkStrNull(line(0)).trim,checkStrNull(line(1)).trim
,checkStrNull(line(2)).trim,checkStrNull(line(3)).trim,checkStrNull(line(4)).trim,checkStrNul
l(line(5)).trim,checkNumericNull(line(6)).trim.toInt,checkNumericNull(line(7)).trim.toDoub
le,checkNumericNull(line(8)).trim.toInt,checkNumericNull(line(9)).trim.toDouble,checkNu
mericNull(line(10)).trim.toInt));
This has the required conversion and checking.
Now I developed Spark SQL UDF to handle the data conversion problem, So my code goes
like this
3. df.registerTempTable("cricket_data");
val result = sqlContext.sql("select name,year,case when month in (10,11,12) then 'Q4'
when month in (7,8,9) then 'Q3' when month in (4,5,6) then 'Q2' when month in(1,2,3)
then 'Q1' end Quarter, run_scored from (select
name,year(convert(REPLACE(date_of_match,'/','-'))) as
year,month(convert(REPLACE(date_of_match,'/','-'))) as month,run_scored from
cricket_data) C");
Convert and REPLACE are custom UDF for this Job
Now this query gives me a result like this:
Now in terms of Data Warehouse this is very inefficient data. As the business user
demands summarized data with full visibility throughout the timestamp.
Here in ETL we use a component called “De-Normalizer” [In Informatica]
So it required transformations like:
4. Aggregator has a sorter which sorts the data first and then implements the aggregation.
Now these are costly transformations in terms of ETL. If we are having data volume 1 Billion
it suffers a big time due to less efficient cache and data mapping
Spark gives a brilliant solution to pivot ta the data in a single line:
val result_pivot = result.groupBy("name","year").pivot("Quarter").agg(sum("run_scored"))
This is an action which pivots the data and transposes huge volume of data within few
minutes.
The data goes like this:
Explain Plan for the Query
5. Explain Plan for the Pivot
We Load this summarized data in hive and show to the End user , So this how my table got
stored in hive.
Data in Hive