Using Redash for SQL Analytics on DatabricksDatabricks
This talk gives a brief overview with a demo performing SQL analytics with Redash and Databricks. We will introduce some of the new features coming as part of our integration with Databricks following the acquisition earlier this year, along with a demo of the other Redash features that enable a productive SQL experience on top of Delta Lake.
Massive Data Processing in Adobe Using Delta LakeDatabricks
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
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.
Marquez: A Metadata Service for Data Abstraction, Data Lineage, and Event-bas...Willy Lulciuc
At WeWork, it's critical that we understand the complete context for all datasets. We also want to be able to explore dependencies between jobs and the datasets they produce and consume. To do this, WeWork needs metadata. In this talk I will focus on Marquez, a core service for the collection, aggregation and visualization of a data ecosystems metadata. Marquez maintains the provenance of how datasets are consumed and produced while providing global visibility into job runtime.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Mind Map of Big Data Technologies and ConceptsAmir Hadad
My first try to capture #bigdata related concepts and technologies in a #mindmap!
#NoSQL vs #SQL https://db-engines.com/
#Streaming vs #Batchprocessing https://lnkd.in/gNEdsrC
#BigDataSecurity https://lnkd.in/gA8e7RC
#OLAP https://lnkd.in/gh3z6r9
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.
Using Redash for SQL Analytics on DatabricksDatabricks
This talk gives a brief overview with a demo performing SQL analytics with Redash and Databricks. We will introduce some of the new features coming as part of our integration with Databricks following the acquisition earlier this year, along with a demo of the other Redash features that enable a productive SQL experience on top of Delta Lake.
Massive Data Processing in Adobe Using Delta LakeDatabricks
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
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.
Marquez: A Metadata Service for Data Abstraction, Data Lineage, and Event-bas...Willy Lulciuc
At WeWork, it's critical that we understand the complete context for all datasets. We also want to be able to explore dependencies between jobs and the datasets they produce and consume. To do this, WeWork needs metadata. In this talk I will focus on Marquez, a core service for the collection, aggregation and visualization of a data ecosystems metadata. Marquez maintains the provenance of how datasets are consumed and produced while providing global visibility into job runtime.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Mind Map of Big Data Technologies and ConceptsAmir Hadad
My first try to capture #bigdata related concepts and technologies in a #mindmap!
#NoSQL vs #SQL https://db-engines.com/
#Streaming vs #Batchprocessing https://lnkd.in/gNEdsrC
#BigDataSecurity https://lnkd.in/gA8e7RC
#OLAP https://lnkd.in/gh3z6r9
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.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
*** Apache Spark and Scala Certification Training: https://www.edureka.co/apache-spark-scala-training ***
This Edureka PPT on "RDD Using Spark" will provide you the detailed and comprehensive knowledge about RDD, which are considered to be the backbone of Apache Spark. You will learn about the various Transformations and Actions that can be performed on RDDs. This PPT will cover the following topics:
Need for RDDs
What are RDDs?
Features of RDDs
Creation of RDDs using Spark
Operations performed on RDDs
RDDs using Spark: Pokemon Use Case
Blog Series: http://bit.ly/2VRogGx
Complete Apache Spark and Scala playlist: http://bit.ly/2In8IXD
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.
Impacts of Sharding, Partitioning, Encoding, and Sorting on Distributed Query...InfluxData
InfluxDB IOx Tech Talks
This talk presents a design of a distributed database system that splits data to gain query performance. The talk will define four main properties of data splitting: sharding, partitioning, sorting, and encoding; and then delve into examples to show their impacts on query performance.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
This document discusses best practices for using PySpark. It covers:
- Core concepts of PySpark including RDDs and the execution model. Functions are serialized and sent to worker nodes using pickle.
- Recommended project structure with modules for data I/O, feature engineering, and modeling.
- Writing testable, serializable code with static methods and avoiding non-serializable objects like database connections.
- Tips for testing like unit testing functions and integration testing the full workflow.
- Best practices for running jobs like configuring the Python environment, managing dependencies, and logging to debug issues.
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Considerations for Data Access in the LakehouseDatabricks
Organizations are increasingly exploring lakehouse architectures with Databricks to combine the best of data lakes and data warehouses. Databricks SQL Analytics introduces new innovation on the “house” to deliver data warehousing performance with the flexibility of data lakes. The lakehouse supports a diverse set of use cases and workloads that require distinct considerations for data access. On the lake side, tables with sensitive data require fine-grained access control that are enforced across the raw data and derivative data products via feature engineering or transformations. Whereas on the house side, tables can require fine-grained data access such as row level segmentation for data sharing, and additional transformations using analytics engineering tools. On the consumption side, there are additional considerations for managing access from popular BI tools such as Tableau, Power BI or Looker.
The product team at Immuta, a Databricks partner, will share their experience building data access governance solutions for lakehouse architectures across different data lake and warehouse platforms to show how to set up data access for common scenarios for Databricks teams new to SQL Analytics.
Top 10 Mistakes When Migrating From Oracle to PostgreSQLJim Mlodgenski
As more and more people are moving to PostgreSQL from Oracle, a pattern of mistakes is emerging. They can be caused by the tools being used or just not understanding how PostgreSQL is different than Oracle. In this talk we will discuss the top mistakes people generally make when moving to PostgreSQL from Oracle and what the correct course of action.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks
Over the years, there has been extensive and continuous effort on improving Spark SQL’s query optimizer and planner, in order to generate high quality query execution plans. One of the biggest improvements is the cost-based optimization framework that collects and leverages a variety of data statistics (e.g., row count, number of distinct values, NULL values, max/min values, etc.) to help Spark make better decisions in picking the most optimal query plan.
This document provides an overview of NoSQL databases and summarizes key information about several NoSQL databases, including HBase, Redis, Cassandra, MongoDB, and Memcached. It discusses concepts like horizontal scalability, the CAP theorem, eventual consistency, and data models used by different NoSQL databases like key-value, document, columnar, and graph structures.
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
SQL Analytics Powering Telemetry Analysis at ComcastDatabricks
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience.
In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses.
We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
*** Apache Spark and Scala Certification Training: https://www.edureka.co/apache-spark-scala-training ***
This Edureka PPT on "RDD Using Spark" will provide you the detailed and comprehensive knowledge about RDD, which are considered to be the backbone of Apache Spark. You will learn about the various Transformations and Actions that can be performed on RDDs. This PPT will cover the following topics:
Need for RDDs
What are RDDs?
Features of RDDs
Creation of RDDs using Spark
Operations performed on RDDs
RDDs using Spark: Pokemon Use Case
Blog Series: http://bit.ly/2VRogGx
Complete Apache Spark and Scala playlist: http://bit.ly/2In8IXD
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.
Impacts of Sharding, Partitioning, Encoding, and Sorting on Distributed Query...InfluxData
InfluxDB IOx Tech Talks
This talk presents a design of a distributed database system that splits data to gain query performance. The talk will define four main properties of data splitting: sharding, partitioning, sorting, and encoding; and then delve into examples to show their impacts on query performance.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
This document discusses best practices for using PySpark. It covers:
- Core concepts of PySpark including RDDs and the execution model. Functions are serialized and sent to worker nodes using pickle.
- Recommended project structure with modules for data I/O, feature engineering, and modeling.
- Writing testable, serializable code with static methods and avoiding non-serializable objects like database connections.
- Tips for testing like unit testing functions and integration testing the full workflow.
- Best practices for running jobs like configuring the Python environment, managing dependencies, and logging to debug issues.
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Considerations for Data Access in the LakehouseDatabricks
Organizations are increasingly exploring lakehouse architectures with Databricks to combine the best of data lakes and data warehouses. Databricks SQL Analytics introduces new innovation on the “house” to deliver data warehousing performance with the flexibility of data lakes. The lakehouse supports a diverse set of use cases and workloads that require distinct considerations for data access. On the lake side, tables with sensitive data require fine-grained access control that are enforced across the raw data and derivative data products via feature engineering or transformations. Whereas on the house side, tables can require fine-grained data access such as row level segmentation for data sharing, and additional transformations using analytics engineering tools. On the consumption side, there are additional considerations for managing access from popular BI tools such as Tableau, Power BI or Looker.
The product team at Immuta, a Databricks partner, will share their experience building data access governance solutions for lakehouse architectures across different data lake and warehouse platforms to show how to set up data access for common scenarios for Databricks teams new to SQL Analytics.
Top 10 Mistakes When Migrating From Oracle to PostgreSQLJim Mlodgenski
As more and more people are moving to PostgreSQL from Oracle, a pattern of mistakes is emerging. They can be caused by the tools being used or just not understanding how PostgreSQL is different than Oracle. In this talk we will discuss the top mistakes people generally make when moving to PostgreSQL from Oracle and what the correct course of action.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks
Over the years, there has been extensive and continuous effort on improving Spark SQL’s query optimizer and planner, in order to generate high quality query execution plans. One of the biggest improvements is the cost-based optimization framework that collects and leverages a variety of data statistics (e.g., row count, number of distinct values, NULL values, max/min values, etc.) to help Spark make better decisions in picking the most optimal query plan.
This document provides an overview of NoSQL databases and summarizes key information about several NoSQL databases, including HBase, Redis, Cassandra, MongoDB, and Memcached. It discusses concepts like horizontal scalability, the CAP theorem, eventual consistency, and data models used by different NoSQL databases like key-value, document, columnar, and graph structures.
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
SQL Analytics Powering Telemetry Analysis at ComcastDatabricks
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience.
In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses.
We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Why does Microsoft care about NoSQL, SQL and Polyglot Persistence?brianlangbecker
This webinar discusses polyglot persistence, which is the strategy of using multiple data storage technologies together to solve different data problems. It explains that while relational databases are good for transactions and consistency, NoSQL databases are better for scale and unstructured data. The webinar shows how to integrate SQL and NoSQL databases by routing requests based on data type or synchronizing data automatically between the databases. It provides an example architecture using a SQL database for legacy apps and reporting with a NoSQL database for mobile and web apps, and discusses benefits like scalability, accelerated development, and leveraging existing tools.
Customer migration to azure sql database from on-premises SQL, for a SaaS app...George Walters
Why would someone take a working on-premises SaaS infrastructure, and migrate it to Azure? We review the technology decisions behind this conversion, and business choices behind migrating to Azure. The SQL 2012 infrastructure and application was migrated to PaaS Services. Finally, how would we do this architecture in 2019.
In Apache Cassandra Lunch #119, Rahul Singh will cover a refresher on GUI desktop/web tools for users that want to get their hands dirty with Cassandra but don't want to deal with CQLSH to do simple queries. Some of the tools are web-based and others are installed on your desktop. Since the beginning days of Cassandra, a lot has changed and there are many options for command-line-haters to use Cassandra.
This document presents an introduction to NoSQL databases. It begins with an overview comparing SQL and NoSQL databases, describing the architecture of NoSQL databases. Examples of different types of NoSQL databases are provided, including key-value stores, column family stores, document databases and graph databases. MapReduce programming is also introduced. Popular NoSQL databases like Cassandra, MongoDB, HBase, and CouchDB are described. The document concludes that NoSQL is well-suited for large, highly distributed data problems.
QuerySurge Slide Deck for Big Data Testing WebinarRTTS
This is a slide deck from QuerySurge's Big Data Testing webinar.
Learn why Testing is pivotal to the success of your Big Data Strategy .
Learn more at www.querysurge.com
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data, Hadoop and NoSQL. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
This information is geared towards:
- Big Data & Data Warehouse Architects,
- ETL Developers
- ETL Testers, Big Data Testers
- Data Analysts
- Operations teams
- Business Intelligence (BI) Architects
- Data Management Officers & Directors
You will learn how to:
- Improve your Data Quality
- Accelerate your data testing cycles
- Reduce your costs & risks
- Provide a huge ROI (as high as 1,300%)
This issue of Dr. Dobb's Journal discusses various topics related to big data. The guest editorial discusses how after distancing themselves from SQL, NoSQL products are now moving toward more transactional models as "NewSQL" gains popularity. An article applies the lambda architecture to a Hadoop project matching social media connections. Another article discusses using Storm for real-time big data analysis as an alternative to Hadoop. The issue also includes news briefs on tools and platforms, an open-source dashboard, and an article on understanding what big data can deliver.
Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuickSight’s in-memory calculation engine – you can go from data to insights, faster than ever.
Azure SQL DB Managed Instances Built to easily modernize application data layerMicrosoft Tech Community
The document discusses Azure SQL Database Managed Instance, a new fully managed database service that provides SQL Server compatibility. It offers seamless migration of SQL Server workloads to the cloud with full compatibility, isolation, security and manageability. Customers can realize up to a 406% ROI over on-premises solutions through lower TCO, automatic management and scaling capabilities.
Introduction to microsoft sql server 2008 r2Eduardo Castro
In this presentation we review the new features in SQL 2008 R2.
Regards,
Ing. Eduardo Castro Martinez, PhD
http://comunidadwindows.org
http://ecastrom.blogspot.com
The document provides an agenda for a 3-day training on data warehousing and business intelligence using Microsoft SQL Server 2005. Day 3 focuses on SQL Server Integration Services (SSIS), including an introduction to SSIS, workshops and exercises on SSIS and SQL Server Analysis Services (SSAS). It also discusses how to create SSIS packages to extract, transform and load data.
This document discusses using Azure DevOps for database development. It provides an overview of Azure DevOps features like source control, work tracking, code reviews, builds and releases. SQL Server Data Tools can be used to create database projects in Azure DevOps. An example is provided of adding a new feature to an AdventureWorks database project, committing the changes to source control, and linking work items to track the task. Data professionals are encouraged to use these tools to version database code and automate deployments.
The document discusses assessing and planning SQL database migrations to Azure. It outlines the steps involved, including initiating and discovering databases, assessing requirements and dependencies, planning the target platform of IaaS or PaaS, migrating the databases with various tools depending on downtime windows, and optimizing workloads in the cloud. It provides examples of tools like MAP, DMA, and migration options like transactional replication or Azure Database Migration Service.
Custom application development according to Oracle is primarily relevant for extending SaaS applications and creating customer experiences. The current recommended approach for building graphical user interface (on web and mobile) is through low code Visual Builder with high code JET injections when required. An alternative low code stack is available from Oracle in the form of APEX, This slide set discusses the above as well as ADF and Forms. It then introduces Digital Assistant, talks about the state and future of Java and concludes with CI/CD and DevOps. As presented on November 5th 2018 at AMIS HQ, Nieuwegein, The Netherlands.
The annual review session by the AMIS team on their findings, interpretations and opinions regarding news, trends, announcements and roadmaps around Oracle's product portfolio.
Similar to Redash: Open Source SQL Analytics on Data Lakes (20)
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized PlatformDatabricks
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Why APM Is Not the Same As ML MonitoringDatabricks
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixDatabricks
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature AggregationsDatabricks
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and SparkDatabricks
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Machine Learning CI/CD for Email Attack DetectionDatabricks
Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models.
In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.
Jeeves Grows Up: An AI Chatbot for Performance and QualityDatabricks
Sarah: CEO-Finance-Report pipeline seems to be slow today. Why
Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days.
Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours.
Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert.
We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example:
Tom: I am not seeing any data for today in my Campaign Metrics Dashboard.
Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021.
This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + FugueDatabricks
This presentation introduces Tune and Fugue, frameworks for intuitive and scalable hyperparameter optimization (HPO). Tune supports both non-iterative and iterative HPO problems. For non-iterative problems, Tune supports grid search, random search, and Bayesian optimization. For iterative problems, Tune generalizes algorithms like Hyperband and Asynchronous Successive Halving. Tune allows tuning models both locally and in a distributed manner without code changes. The presentation demonstrates Tune's capabilities through examples tuning Scikit-Learn and Keras models. The goal of Tune and Fugue is to make HPO development easy, testable, and scalable.
When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed.
As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations.
In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment
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."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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
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.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
4. Agenda
Introduction to Redash on Databricks
Product Walkthrough
Use Cases & Redash in Action
Closing & Next Steps
Q&A
5. What is Redash?
Redash is the easiest way to query, visualize,
and share insights from your data lake.
It’s an open source project, integrated into
Databricks
It gives SQL-native users a simple but powerful
interface to the power of Databricks.
● SQL Box
● Fast Visualizations / Dashboards
● Scheduled Queries / Alerts
● Data Sources
● Quick share links
6. Redash on Databricks: Who is it for?
Data Science
Data Engineering
SQL Analytics
&
Sharing
7. What can Redash do?
● Easy integration with Databricks
● Simple SQL Editor (Spark SQL, Autocomplete)
● Fast Results → Visualizations → Dashboards
● Flexible execution:
○ Parameters
○ Scheduled Queries
○ Alerts
● REST API
● Share data and insights seamlessly across your company
(and dozens more + most REST APIs)