Apache HAWQ Architecture

Data Con LA 2020 Description Apache Druid is a cloud-native open-source database that enables developers to build highly-scalable, low-latency, real-time interactive dashboards and apps to explore huge quantities of data. This column-oriented database provides the microsecond query response times required for ad-hoc queries and programmatic analytics. Druid natively streams data from Apache Kafka (and more) and batch loads just about anything. At ingestion, Druid partitions data based on time so time-based queries run significantly faster than traditional databases, plus Druid offers SQL compatibility. Druid is used in production by AirBnB, Nielsen, Netflix and more for real-time and historical data analytics. This talk provides an introduction to Apache Druid including: Druid's core architecture and its advantages, Working with streaming and batch data in Druid, Querying data and building apps on Druid and Real-world examples of Apache Druid in action Speaker Matt Sarrel, Imply Data, Developer Evangelist

Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.    Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.

"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"

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.

Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt. Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com. Chris Riccomini works as a Software Engineer at WePay.

In data analytics frameworks such as Spark it is important to detect and avoid scanning data that is irrelevant to the executed query, an optimization which is known as partition pruning. Dynamic partition pruning occurs when the optimizer is unable to identify at parse time the partitions it has to eliminate. In particular, we consider a star schema which consists of one or multiple fact tables referencing any number of dimension tables. In such join operations, we can prune the partitions the join reads from a fact table by identifying those partitions that result from filtering the dimension tables. In this talk we present a mechanism for performing dynamic partition pruning at runtime by reusing the dimension table broadcast results in hash joins and we show significant improvements for most TPCDS queries.

When interacting with analytics dashboards, in order to achieve a smooth user experience, two major key requirements are quick response time and data freshness. To meet the requirements of creating fast interactive BI dashboards over streaming data, organizations often struggle with selecting a proper serving layer. Cluster computing frameworks such as Hadoop or Spark work well for storing large volumes of data, although they are not optimized for making it available for queries in real time. Long query latencies also make these systems suboptimal choices for powering interactive dashboards and BI use cases. This talk presents an open source real time data analytics stack using Apache Kafka, Druid, and Superset. The stack combines the low-latency streaming and processing capabilities of Kafka with Druid, which enables immediate exploration and provides low-latency queries over the ingested data streams. Superset provides the visualization and dashboarding that integrates nicely with Druid. In this talk we will discuss why this architecture is well suited to interactive applications over streaming data, present an end-to-end demo of complete stack, discuss its key features, and discuss performance characteristics from real-world use cases. Speaker Nishant Bangarwa, Software Engineer, Hortonworks

At Facebook, millions of Hive queries are executed on a daily basis, and the workload contributes to important analytics that drive product decisions and insights. Spark SQL in Apache Spark provides much of the same functionality as Hive query language (HQL) more efficiently, and Facebook is building a framework to migrate existing production Hive workload to Spark SQL with minimal user intervention. Before Facebook began large-scale migration to SparkSQL, they worked on identifying the gap between HQL and SparkSQL. They built an offline syntax analysis tool that parses, analyzes, optimizes and generates physical plans on daily HQL workload. In this session, they’ll share their results. After finding their syntactic analysis encouraging, they built tooling for offline semantic analysis where they run HQL queries in their Spark shadow cluster and validate the outputs. Output validation is necessary since the runtime behavior in Spark SQL may be different from HQL. They have built a migration framework that supports HQL in both Hive and Spark execution engines, can shadow and validate HQL workloads in Spark, and makes it easy for users to convert their workloads.

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.

This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering: - How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs. - Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics. - The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift. - The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database. Created by: Rahul Pathak, Sr. Manager of Software Development

Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.

Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.

Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.

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.

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.

Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.

It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together.  In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers.  We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS  as well as high-level concepts such as when to use a data lake.  We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.

Data warehousing is a critical component for analysing and extracting actionable insights from your data. Amazon Redshift allows you to deploy a scalable data warehouse in a matter of minutes and starts to analyse your data right away using your existing business intelligence tools.

The document discusses developing data APIs for the Arabidopsis Information Portal (AIP) to enable discovery and reuse of services, data, and codes. It describes the AIP strategy of centralized data warehousing with infrastructure for data federation through web services and standards like REST. The AIP architecture includes an API manager, services bus and mediators to integrate diverse data sources and legacy systems while providing authentication, documentation, logging and versioning.

This document discusses a presentation on architecting Hadoop application architectures for a next generation data platform. It provides an overview of the presentation topics which include a case study on using Hadoop for an Internet of Things and entity 360 application. It introduces the key components of the proposed high level architecture including ingesting streaming and batch data using Kafka and Flume, stream processing with Kafka streams and storage in Hadoop.

"The only constant in life is change! The same applies to your Kafka events flowing through your streaming applications. The Confluent Schema Registry allows us to control how schemas can evolve over time without breaking the compatibility of our streaming applications. But when you start with Kafka and (Avro) schemas, this can be pretty overwhelming. Join Kosta and Tim as we dive into the tricky world of backward and forward compatibility in schema design. During this deep dive talk, we are going to answer questions like: * What compatibility level to pick? * What changes can I make when evolving my schemas? * What options do I have when I need to introduce a breaking change? * Should we automatically register schemas from our applications? Or do we need a separate step in our deployment process to promote schemas to higher-level environments? * What to promote first? My producer, consumer or schema? * How do you generate Java classes from your Avro schemas using Maven or Gradle, and how to integrate this into your project(s)? * How do you build an automated test suite (unit tests) to gain more confidence and verify you are not breaking compatibility? Even before deploying a new version of your schema or application. With live demos, we'll show you how to make schema changes work seamlessly. Emphasizing the crucial decisions, using real-life examples, pitfalls and best practices when promoting schemas on the consumer and producer sides. Explore the ins and outs of Apache Avro and the Schema Registry with us at the Kafka Summit! Start evolving your schemas in a better way today, and join this talk!"

Hadoop application architectures - using Customer 360 (more generally, Entity 360) as an example. By Ted Malaska, Jonathan Seidman and Mark Grover at Strata + Hadoop World 2016 in NYC.

Slides from a talk given by Julian Hyde to the Hive User Group Meetup NYC Edition, on October 15th, 2014.

Talk at Apache Drill Meetup (November 2018) describing how to accelerate SQL queries in a NoSQL database using Apache Drill and Secondary Indexes. Drill (in conjunction with Apache Calcite) provides a comprehensive cost-based index planning and execution framework. Queries with indexed columns in the WHERE clause, ORDER BY, GROUP BY and Joins can be sped up substantially. A reference implementation with MapR-DB JSON database is described.

A tutorial on architecting a next generation data platform by Jonathan Seidman, Gwen Shapira and Mark Grover at Strata New York 2017.

This document discusses a high-level architecture for analyzing taxi trip data in real-time and batch using Apache Hadoop and streaming technologies. The architecture includes ingesting data from multiple sources using Kafka, processing streaming data using stream processing engines, storing data in data stores like HDFS, and enabling real-time and batch querying and analytics. Key considerations discussed are choosing data transport and stream processing technologies, scaling and reliability, and processing both streaming and batch data.

Presentation on Architectural Patterns for Streaming Applications at Strata + Hadoop World Singapore 2015

Enterprise data is moving into Hadoop, but some data has to stay in operational systems. Apache Calcite (the technology behind Hive’s new cost-based optimizer, formerly known as Optiq) is a query-optimization and data federation technology that allows you to combine data in Hadoop with data in NoSQL systems such as MongoDB and Splunk, and access it all via SQL. Hyde shows how to quickly build a SQL interface to a NoSQL system using Calcite. He shows how to add rules and operators to Calcite to push down processing to the source system, and how to automatically build materialized data sets in memory for blazing-fast interactive analysis.

Presented at the O'Reilly Velocity 2017 Conference in NY, on building a framework to support resilient near-realtime data pipelines

This document provides an overview of a presentation on being a polyglot data scientist using multiple languages and tools. It discusses using SQL, R, and Python together in data science work. The presentation covers the challenges of being a polyglot, how SQL Server with R or Python can help solve problems more easily, and examples of analyzing sensor data with these tools. It also discusses resources for learning more about R, Python, and machine learning services in SQL Server.

APEX 5 Interactive Reports (IR) are powerful out of the box, but one can significantly improve performance by understanding a few key settings.

APEX 5 Interactive Reports (IR) are powerful out of the box, but one can significantly improve performance by strategic settings of certain key parameters. The full presentation covers all the options.

This document introduces Prometheus, an open-source monitoring system that allows instrumentation of everything including RPCs, interfaces, business logic, and logs. It provides client libraries that make instrumentation easy across many languages. The Prometheus server can handle over a million time series in one instance with no dependencies. It offers dashboards, expression queries, alerts and integrates with many systems. Time series have structured labels allowing flexible aggregation and complex math for rules and alerts. Prometheus costs less than $.001 per time series per month and is developed by SoundCloud, Boxever and Docker with an active community.

Description of some of the elements that go in to creating a PostgreSQL-as-a-Service for organizations with many teams and a diverse ecosystem of applications and teams.

Outlines the CSS and JavaScript changes in APEX 5 Interactive Reports, recommending supported APIs and some unsupported options for customizing were necessary. Discusses and dmeonstrates how typical declarative settings influence end-user performance. LEarn how to leverage IR settings to maximize end user performance.

OpenTSDB is used at Criteo for monitoring their large Hadoop infrastructure which includes over 2500 servers running many different services like HDFS, YARN, HBase, Kafka, and Storm. OpenTSDB was chosen because it can handle the scale of metrics collected, store metrics for long periods of time with fine-grained resolution, and is easily extensible to add new metrics. It uses HBase for storage which is optimized for the time series data stored in OpenTSDB and can scale to meet Criteo's needs of storing billions of data points and handling high query loads.

By Rajiv Kurian, software engineer at SignalFx. At SignalFx, we deal with high-volume high-resolution data from our users. This requires a high performance ingest pipeline. Over time we’ve found that we needed to adapt architectural principles from specialized fields such as HPC to get beyond performance plateaus encountered with more generic approaches. Some key examples include: * Write very simple single threaded code, instead of complex algorithms * Parallelize by running multiple copies of simple single threaded code, instead of using concurrent algorithms * Separate the data plane from the control plane, instead of slowing data for control * Write compact, array-based data structures with minimal indirection, instead of pointer-based data structures and uncontrolled allocation

This is the presentation I made on the Hadoop User Group Ireland meetup in Dublin. It covers the main ideas of both MPP, Hadoop and the distributed systems in general, and also how to chose the best option for you

This is the presentation I delivered on Hadoop User Group Ireland meetup in Dublin on Nov 28 2015. It covers at glance the architecture of GPDB and most important its features. Sorry for the colors - Slideshare is crappy with PDFs

This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark

This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture

Презентация с highload++ 2015 по архитектуре Apache HAWQ на русском языке

The presentation delivered during Hadoop Kitchen in Moscow on 27.09.2014. It describes the technology that lies beneath Pivotal HAWQ technology

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이 세션에서는 SageMaker Training Jobs / SageMaker Jumpstart를 사용하여 Foundation Model 을 Pre-Triaining 하거나 Fine Tuing 하는 방안을 제시합니다. 이 세션을 통해 아래 3가지가 소개됩니다. 1. 파운데이션 모델을 처음부터 Training 2. 오픈 소스 모델을 사용하여 파운데이션 모델을 Pre-Training 3. 도메인에 맞게 모델을 Fine Tuning하는 방안 발표자: Miron Perel, Principal ML GTM Specialist, AWS Kristine Pearce, Principal ML BD, AWS

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Airline Satisfaction Project using Azure This presentation is created as a foundation of understanding and comparing data science/machine learning solutions made in Python notebooks locally and on Azure cloud, as a part of Course DP-100 - Designing and Implementing a Data Science Solution on Azure.

Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합니다.

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간단해 보이지만 실제로는 복잡한 몇 가지 Amazon DynamoDB 디자인 퍼즐을 함께 해결하며 DynamoDB가 대규모로 작동하는 방식에 대해 자세히 알아봅니다. DynamoDB의 작동 방식을 이해함으로써 더 효과적이고 확장 가능한 솔루션을 찾는 방법을 알아보세요.

LLM powered contract compliance application which uses Advanced RAG method Self-RAG and Knowledge Graph together for the first time. It provides highest accuracy for contract compliance recorded so far for Oil and Gas Industry.

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Aurora PostgreSQL에서 가장 일반적인 performance use case 들에 대해 Aurora PostreSQL의 모니터링 Tool들을 통해 어떤게 문제를 식별하고 분석하는지 그리고 이 문제를 해결해나가는 절차와 방법에 대한 Deep Dive입니다.

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