Big data processing with Apache Spark and Oracle Database

Parquet is a columnar storage format for Hadoop data. It was developed by Twitter and Cloudera to optimize storage and querying of large datasets. Parquet provides more efficient compression and I/O compared to traditional row-based formats by storing data by column. Early results show a 28% reduction in storage size and up to a 114% improvement in query performance versus the original Thrift format. Parquet supports complex nested schemas and can be used with Hadoop tools like Hive, Pig, and Impala.

This document discusses Delta Change Data Feed (CDF), which allows capturing changes made to Delta tables. It describes how CDF works by storing change events like inserts, updates and deletes. It also outlines how CDF can be used to improve ETL pipelines, unify batch and streaming workflows, and meet regulatory needs. The document provides examples of enabling CDF, querying change data and storing the change events. It concludes by offering a demo of CDF in Jupyter notebooks.

The document discusses updates to InfluxDB IOx, a new columnar time series database. It covers changes and improvements to the API, CLI, query capabilities, and path to open sourcing builds. Key points include moving to gRPC for management, adding PostgreSQL string functions to queries, optimizing functions for scalar values and columns, and monitoring internal systems as the first step to releasing open source builds.

Apache Spark 2.1.0 boosted the performance of Apache Spark SQL due to Project Tungsten software improvements. Another 16x times faster has been achieved by using Oracle’s innovations for Apache Spark SQL. This 16x improvement is made possible by using Oracle’s Software in Silicon accelerator offload technologies. Apache Spark SQL In-memory performance is becoming more important due to many factors. Users are now performing more advanced SQL processing on multi-terabyte workloads. In addition on-prem and cloud servers are getting larger physical memory to enable storing these huge workloads be stored in memory. In this talk we will look at using Spark SQL in feature creation, feature generation within pipelines for Spark ML. This presentation will explore workloads at scale and with complex interactions. We also provide best practices and tuning suggestion to support these kinds of workloads on real applications in cloud deployments. In addition ideas for next generation Tungsten project will also be discussed.

In financial world, petabytes of transactional data need to be stored, processed, distributed across global customers and partners in a secured, compliant and accurate way with high availability, resiliency and observability. In American Express, we need to generate hundreds of different kinds of reports and distribute to thousands of partners in different schedules based on billions of daily transactions. Our next generation reporting framework is a highly configurable enterprise framework that caters to different reporting needs with zero development. This reusable framework entails dynamic scheduling of partner-specific reports, transforming, aggregating and filtering the data into different dataframes using inbuilt as well as user-defined spark functions leveraging spark’s in memory and parallel processing capabilities. This also encompasses applying business rules and converting it into different formats by embedding template engines like FreeMarker and Mustache into the framework.

Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018. Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.

As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements. 1) Generality: support reading/writing most data management/storage systems. 2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities. Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.

What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet. At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it. We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).

Most query engines follow an interpreter-based approach where a SQL query is translated into a tree of relational algebra operations then fed through a conventional tuple-based iterator model to execute the query. We will explore the overhead associated with this approach and how the performance of query execution on columnar data can be improved using run-time code generation via LLVM. Generally speaking, the best case for optimal query execution performance is a hand-written query plan that does exactly what is needed by the query for the exact same data types and format. Vectorized query processing models amortize the cost of function calls. However, research has shown that hand-written code for a given query plan has the potential to outperform the optimizations associated with a vectorized query processing model. Over the last decade, the LLVM compiler framework has seen significant development. Furthermore, the database community has realized the potential of LLVM to boost query performance by implementing JIT query compilation frameworks. With LLVM, a SQL query is translated into a portable intermediary representation (IR) which is subsequently converted into machine code for the desired target architecture. Dremio is built on top of Apache Arrow’s in-memory columnar vector format. The in-memory vectors map directly to the vector type in LLVM and that makes our job easier when writing the query processing algorithms in LLVM. We will talk about how Dremio implemented query processing logic in LLVM for some operators like FILTER and PROJECT. We will also discuss the performance benefits of LLVM-based vectorized query execution over other methods. Speaker Siddharth Teotia, Dremio, Software Engineer

This document provides an overview and comparison of the Avro and Parquet data formats. It begins with introductions to Avro and Parquet, describing their key features and uses. The document then covers Avro and Parquet schemas, file structures, and includes code examples. Finally, it discusses considerations for choosing between Avro and Parquet and shares experiences using the two formats.

This has a high level overview of the Apache Arrow and how it applies to a time series database, and contains examples written in Rust

Alluxio Day VI October 12, 2021 https://www.alluxio.io/alluxio-day/ Speaker: Vinoth Chandar, Apache Software Foundation Raymond Xu, Zendesk

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.

Kubernetes has rapidly established itself as the de facto standard for orchestrating containerized infrastructures. And with the recent completion of the refactoring of Flink's deployment and process model known as FLIP-6, Kubernetes has become a natural choice for Flink deployments. In this talk we will walk through how to get Flink running on Kubernetes

The document discusses Apache Flink, an open source stream processing framework. It provides high throughput and low latency processing of both streaming and batch data. Flink allows for explicit handling of event time, stateful stream processing with exactly-once semantics, and high performance. It also supports features like windowing, sessionization, and complex event processing that are useful for building streaming applications.

This document discusses 5 common mistakes when writing Spark applications: 1) Improperly sizing executors by not considering cores, memory, and overhead. The optimal configuration depends on the workload and cluster resources. 2) Applications failing due to shuffle blocks exceeding 2GB size limit. Increasing the number of partitions helps address this. 3) Jobs running slowly due to data skew in joins and shuffles. Techniques like salting keys can help address skew. 4) Not properly managing the DAG to avoid shuffles and bring work to the data. Using ReduceByKey over GroupByKey and TreeReduce over Reduce when possible. 5) Classpath conflicts arising from mismatched library versions, which can be addressed using sh

Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms. Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.

This document provides a summary of Apache Spark, including: - Spark is a framework for large-scale data processing across clusters that is faster than Hadoop by relying more on RAM and minimizing disk IO. - Spark transformations operate on resilient distributed datasets (RDDs) to manipulate data, while actions return results to the driver program. - Spark can receive data from various sources like files, databases, sockets through its datasource APIs and process both batch and streaming data. - Spark streaming divides streaming data into micro-batches called DStreams and integrates with messaging systems like Kafka. Structured streaming is a newer API that works on DataFrames/Datasets.

This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark. Below topics are explained in this Spark presentation: 1. History of Spark 2. What is Spark 3. Hadoop vs Spark 4. Components of Apache Spark 5. Spark architecture 6. Applications of Spark 7. Spark usecase What is this Big Data Hadoop training course about? The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. What are the course objectives? Simplilearn’s Apache Spark and Scala certification training are designed to: 1. Advance your expertise in the Big Data Hadoop Ecosystem 2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark 3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos What skills will you learn? By completing this Apache Spark and Scala course you will be able to: 1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations 2. Understand the fundamentals of the Scala programming language and its features 3. Explain and master the process of installing Spark as a standalone cluster 4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark 5. Master Structured Query Language (SQL) using SparkSQL 6. Gain a thorough understanding of Spark streaming features 7. Master and describe the features of Spark ML programming and GraphX programming Who should take this Scala course? 1. Professionals aspiring for a career in the field of real-time big data analytics 2. Analytics professionals 3. Research professionals 4. IT developers and testers 5. Data scientists 6. BI and reporting professionals 7. Students who wish to gain a thorough understanding of Apache Spark Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training

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.

Apache Spark is an open-source distributed processing engine that is up to 100 times faster than Hadoop for processing data stored in memory and 10 times faster for data stored on disk. It provides high-level APIs in Java, Scala, Python and SQL and supports batch processing, streaming, and machine learning. Spark runs on Hadoop, Mesos, Kubernetes or standalone and can access diverse data sources using its core abstraction called resilient distributed datasets (RDDs).

An Engine to process big data in faster(than MR), easy and extremely scalable way. An Open Source, parallel, in-memory processing, cluster computing framework. Solution for loading, processing and end to end analyzing large scale data. Iterative and Interactive : Scala, Java, Python, R and with Command line interface.

Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.

This document provides an introduction and overview of Apache Spark. It discusses why Spark is useful, describes some Spark basics including Resilient Distributed Datasets (RDDs) and DataFrames, and gives a quick tour of Spark Core, SQL, and Streaming functionality. It also provides some tips for using Spark and describes how to set up Spark locally. The presenter is introduced as a data engineer who uses Spark to load data from Kafka streams into Redshift and Cassandra. Ways to learn more about Spark are suggested at the end.

Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

This introductory workshop is aimed at data analysts & data engineers new to Apache Spark and exposes them how to analyze big data with Spark SQL and DataFrames. In this partly instructor-led and self-paced labs, we will cover Spark concepts and you’ll do labs for Spark SQL and DataFrames in Databricks Community Edition. Toward the end, you’ll get a glimpse into newly minted Databricks Developer Certification for Apache Spark: what to expect & how to prepare for it. * Apache Spark Basics & Architecture * Spark SQL * DataFrames * Brief Overview of Databricks Certified Developer for Apache Spark

Apache Spark presentation at HasGeek FifthElelephant https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries

Presentation detailed about capabilities of In memory Analytic using Apache Spark. Apache Spark overview with programming mode, cluster mode with Mosos, supported operations and comparison with Hadoop Map Reduce. Elaborating Apache Spark Stack expansion like Shark, Streaming, MLib, GraphX

This document discusses Spark Streaming and its use for near real-time ETL. It provides an overview of Spark Streaming, how it works internally using receivers and workers to process streaming data, and an example use case of building a recommender system to find matches using both batch and streaming data. Key points covered include the streaming execution model, handling data receipt and job scheduling, and potential issues around data loss and (de)serialization.

Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS. Learning Objectives: • Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing. • How to deploy and tune scalable clusters running Spark on Amazon EMR. • How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3. • Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.