Economic globalization requires data to be available globally. With most data stored in file systems, solutions to make this data globally available become more important. Files that are in file systems can be protected or shared by replicating these files to another file system that is in a remote location. The remote location might be just around the corner or in a different country. Therefore, the techniques that are used to protect and share files must account for long distances and slow and unreliable wide area network (WAN) connections. IBM® Spectrum Scale is a scalable clustered file system that can be used to store all kinds of unstructured data. It provides open data access by way of Network File System (NFS); Server Message Block (SMB); POSIX Object Storage APIs, such as S3 and OpenStack Swift; and the Hadoop Distributed File System (HDFS) for accessing and sharing data. The IBM Aspera® file transfer solution (IBM Aspera Sync) provides predictable and reliable data transfer across large distance for small and large files. The combination of both can be used for global sharing and protection of data. This IBM Redpaper™ publication describes how IBM Aspera Sync can be used to protect and share data that is stored in IBM Spectrum™ Scale file systems across large distances of several hundred to thousands of miles. We also explain the integration of IBM Aspera Sync with IBM Spectrum Scale™ and differentiate it from solutions that are built into IBM Spectrum Scale for protection and sharing. We also describe different use cases for IBM Aspera Sync with IBM Spectrum Scale.

"Mastering Hadoop 3" is your in-depth guide to understanding and mastering the advanced features of the Hadoop ecosystem. With a focus on distributed computing and data processing, this book covers essential tools such as YARN, MapReduce, and Apache Spark to help you build scalable, efficient data pipelines. What this Book will help me do Gain a comprehensive understanding of Hadoop Distributed File System (HDFS) and YARN for effective resource management. Master data processing with MapReduce and learn to integrate with real-time processing engines like Spark and Flink. Develop and secure enterprise-grade Hadoop-based data pipelines by implementing robust security and governance measures. Explore techniques for batch data processing, data modeling, and designing applications tailored for Hadoop environments. Understand best practices for optimizing and troubleshooting Hadoop clusters for enhanced performance and reliability. Author(s) The authors, including None Wong, None Singh, and None Kumar, bring together years of experience in big data engineering, distributed systems, and enterprise application development. They aim to provide a clear pathway to mastering Hadoop ecosystem tools. Who is it for? This book is ideal for budding big data professionals who have some familiarity with Java and basic Hadoop concepts and wish to elevate their expertise. If you're a Hadoop career practitioner keen to expand your understanding of the ecosystem's advanced capabilities or a professional looking to implement Hadoop in organizational workflows, this book is well-suited for you.

Summary Building internal expertise around big data in a large organization is a major competitive advantage. However, it can be a difficult process due to compliance needs and the need to scale globally on day one. In this episode Jesper Søgaard and Keld Antonsen share the story of starting and growing the big data group at LEGO. They discuss the challenges of being at global scale from the start, hiring and training talented engineers, prototyping and deploying new systems in the cloud, and what they have learned in the process. This is a useful conversation for engineers, managers, and leadership who are interested in building enterprise big data systems.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Keld Antonsen and Jesper Soegaard about the data infrastructure and analytics that powers LEGO

Interview

Introduction How did you get involved in the area of data management? My understanding is that the big data group at LEGO is a fairly recent development. Can you share the story of how it got started?

What kinds of data practices were in place prior to starting a dedicated group for managing the organization’s data? What was the transition process like, migrating data silos into a uniformly managed platform?

What are the biggest data challenges that you face at LEGO? What are some of the most critical sources and types of data that you are managing? What are the main components of the data infrastructure that you have built to support the organizations analytical needs?

What are some of the technologies that you have found to be most useful? Which have been the most problematic?

What does the team structure look like for the data services at LEGO?

Does that reflect in the types/numbers of systems that you support?

What types of testing, monitoring, and metrics do you use to ensure the health of the systems you support? What have been some of the most interesting, challenging, or useful lessons that you have learned while building and maintaining the data platforms at LEGO? How have the data systems at Lego evolved over recent years as new technologies and techniques have been developed? How does the global nature of the LEGO business influence the design strategies and technology choices for your platform? What are you most excited for in the coming year?

Contact Info

Jesper

LinkedIn

Keld

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

LEGO Group ERP (Enterprise Resource Planning) Predictive Analytics Prescriptive Analytics Hadoop Center Of Excellence Continuous Integration Spark

Podcast Episode

Apache NiFi

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary

The past year has been an active one for the timeseries market. New products have been launched, more businesses have moved to streaming analytics, and the team at Timescale has been keeping busy. In this episode the TimescaleDB CEO Ajay Kulkarni and CTO Michael Freedman stop by to talk about their 1.0 release, how the use cases for timeseries data have proliferated, and how they are continuing to simplify the task of processing your time oriented events.

Introduction

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m welcoming Ajay Kulkarni and Mike Freedman back to talk about how TimescaleDB has grown and changed over the past year

Interview

Introduction How did you get involved in the area of data management? Can you refresh our memory about what TimescaleDB is? How has the market for timeseries databases changed since we last spoke? What has changed in the focus and features of the TimescaleDB project and company? Toward the end of 2018 you launched the 1.0 release of Timescale. What were your criteria for establishing that milestone?

What were the most challenging aspects of reaching that goal?

In terms of timeseries workloads, what are some of the factors that differ across varying use cases?

How do those differences impact the ways in which Timescale is used by the end user, and built by your team?

What are some of the initial assumptions that you made while first launching Timescale that have held true, and which have been disproven? How have the improvements and new features in the recent releases of PostgreSQL impacted the Timescale product?

Have you been able to leverage some of the native improvements to simplify your implementation? Are there any use cases for Timescale that would have been previously impractical in vanilla Postgres that would now be reasonable without the help of Timescale?

What is in store for the future of the Timescale product and organization?

Contact Info

Ajay

@acoustik on Twitter LinkedIn

Mike

LinkedIn Website @michaelfreedman on Twitter

Timescale

Website Documentation Careers timescaledb on GitHub @timescaledb on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

TimescaleDB Original Appearance on the Data Engineering Podcast 1.0 Release Blog Post PostgreSQL

Podcast Interview

RDS DB-Engines MongoDB IOT (Internet Of Things) AWS Timestream Kafka Pulsar

Podcast Episode

Spark

Podcast Episode

Flink

Podcast Episode

Hadoop DevOps PipelineDB

Podcast Interview

Grafana Tableau Prometheus OLTP (Online Transaction Processing) Oracle DB Data Lake

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

The Hadoop platform is purpose built for processing large, slow moving data in long-running batch jobs. As the ecosystem around it has grown, so has the need for fast data analytics on fast moving data. To fill this need the Kudu project was created with a column oriented table format that was tuned for high volumes of writes and rapid query execution across those tables. For a perfect pairing, they made it easy to connect to the Impala SQL engine. In this episode Brock Noland and Jordan Birdsell from PhData explain how Kudu is architected, how it compares to other storage systems in the Hadoop orbit, and how to start integrating it into you analytics pipeline.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Brock Noland and Jordan Birdsell about Apache Kudu and how it is able to provide fast analytics on fast data in the Hadoop ecosystem

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Kudu is and the motivation for building it?

How does it fit into the Hadoop ecosystem? How does it compare to the work being done on the Iceberg table format?

What are some of the common application and system design patterns that Kudu supports? How is Kudu architected and how has it evolved over the life of the project? There are many projects in and around the Hadoop ecosystem that rely on Zookeeper as a building block for consensus. What was the reasoning for using Raft in Kudu? How does the storage layer in Kudu differ from what would be found in systems like Hive or HBase?

What are the implementation details in the Kudu storage interface that have had the greatest impact on its overall speed and performance?

A number of the projects built for large scale data processing were not initially built with a focus on operational simplicity. What are the features of Kudu that simplify deployment and management of production infrastructure? What was the motivation for using C++ as the language target for Kudu?

If you were to start the project over today what would you do differently?

What are some situations where you would advise against using Kudu? What have you found to be the most interesting/unexpected/challenging lessons learned in the process of building and maintaining Kudu? What are you most excited about for the future of Kudu?

Contact Info

Brock

LinkedIn @brocknoland on Twitter

Jordan

LinkedIn @jordanbirdsell jbirdsell on GitHub

PhData

Website phdata on GitHub @phdatainc on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Kudu PhData Getting Started with Apache Kudu Thomson Reuters Hadoop Oracle Exadata Slowly Changing Dimensions HDFS S3 Azure Blob Storage State Farm Stanly Black & Decker ETL (Extract, Transform, Load) Parquet

Podcast Episode

ORC HBase Spark

Podcast Episode

Summary

Apache Spark is a popular and widely used tool for a variety of data oriented projects. With the large array of capabilities, and the complexity of the underlying system, it can be difficult to understand how to get started using it. Jean George Perrin has been so impressed by the versatility of Spark that he is writing a book for data engineers to hit the ground running. In this episode he helps to make sense of what Spark is, how it works, and the various ways that you can use it. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jean Georges Perrin, author of the upcoming Manning book Spark In Action 2nd Edition, about the ways that Spark is used and how it fits into the data landscape

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Spark is?

What are some of the main use cases for Spark? What are some of the problems that Spark is uniquely suited to address? Who uses Spark?

What are the tools offered to Spark users? How does it compare to some of the other streaming frameworks such as Flink, Kafka, or Storm? For someone building on top of Spark what are the main software design paradigms?

How does the design of an application change as you go from a local development environment to a production cluster?

Once your application is written, what is involved in deploying it to a production environment? What are some of the most useful strategies that you have seen for improving the efficiency and performance of a processing pipeline? What are some of the edge cases and architectural considerations that engineers should be considering as they begin to scale their deployments? What are some of the common ways that Spark is deployed, in terms of the cluster topology and the supporting technologies? What are the limitations of the Spark programming model?

What are the cases where Spark is the wrong choice?

What was your motivation for writing a book about Spark?

Who is the target audience?

What have been some of the most interesting or useful lessons that you have learned in the process of writing a book about Spark? What advice do you have for anyone who is considering or currently using Spark?

Contact Info

@jgperrin on Twitter Blog

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Book Discount

Use the code poddataeng18 to get 40% off of all of Manning’s products at manning.com

Links

Apache Spark Spark In Action Book code examples in GitHub Informix International Informix Users Group MySQL Microsoft SQL Server ETL (Extract, Transform, Load) Spark SQL and Spark In Action‘s chapter 11 Spark ML and Spark In Action‘s chapter 18 Spark Streaming (structured) and Spark In Action‘s chapter 10 Spark GraphX Hadoop Jupyter

Podcast Interview

Zeppelin Databricks IBM Watson Studio Kafka Flink

P

Dive into "Hands-On Data Science with R" and embark on a journey to master the R language for practical data science applications. This comprehensive guide walks through data manipulation, visualization, and advanced analytics, preparing you to tackle real-world data challenges with confidence. What this Book will help me do Understand how to utilize popular R packages effectively for data science tasks. Learn techniques for cleaning, preprocessing, and exploring datasets. Gain insights into implementing machine learning models in R for predictive analytics. Master the use of advanced visualization tools to extract and communicate insights. Develop expertise in integrating R with big data platforms like Hadoop and Spark. Author(s) This book was written by experts in data science and R including Doug Ortiz and his co-authors. They bring years of industry experience and a desire to teach, presenting complex topics in an approachable manner. Who is it for? Designed for data analysts, statisticians, or programmers with basic R knowledge looking to dive into machine learning and predictive analytics. If you're aiming to enhance your skill set or gain confidence in tackling real-world data problems, this book is an excellent choice.

Summary

When your data lives in multiple locations, belonging to at least as many applications, it is exceedingly difficult to ask complex questions of it. The default way to manage this situation is by crafting pipelines that will extract the data from source systems and load it into a data lake or data warehouse. In order to make this situation more manageable and allow everyone in the business to gain value from the data the folks at Dremio built a self service data platform. In this episode Tomer Shiran, CEO and co-founder of Dremio, explains how it fits into the modern data landscape, how it works under the hood, and how you can start using it today to make your life easier.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Tomer Shiran about Dremio, the open source data as a service platform

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Dremio is and how the project and business got started?

What was the motivation for keeping your primary product open source? What is the governance model for the project?

How does Dremio fit in the current landscape of data tools?

What are some use cases that Dremio is uniquely equipped to support? Do you think that Dremio obviates the need for a data warehouse or large scale data lake?

How is Dremio architected internally?

How has that architecture evolved from when it was first built?

There are a large array of components (e.g. governance, lineage, catalog) built into Dremio that are often found in dedicated products. What are some of the strategies that you have as a business and development team to manage and integrate the complexity of the product?

What are the benefits of integrating all of those capabilities into a single system? What are the drawbacks?

One of the useful features of Dremio is the granular access controls. Can you discuss how those are implemented and controlled? For someone who is interested in deploying Dremio to their environment what is involved in getting it installed?

What are the scaling factors?

What are some of the most exciting features that have been added in recent releases? When is Dremio the wrong choice? What have been some of the most challenging aspects of building, maintaining, and growing the technical and business platform of Dremio? What do you have planned for the future of Dremio?

Contact Info

Tomer

@tshiran on Twitter LinkedIn

Dremio

Website @dremio on Twitter dremio on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Dremio MapR Presto Business Intelligence Arrow Tableau Power BI Jupyter OLAP Cube Apache Foundation Hadoop Nikon DSLR Spark ETL (Extract, Transform, Load) Parquet Avro K8s Helm Yarn Gandiva Initiative for Apache Arrow LLVM TLS

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Fabian Hueske, co-author of the upcoming O’Reilly book Stream Processing With Apache Flink, about his work on Apache Flink, the stateful streaming engine

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Flink is and how the project got started? What are some of the primary ways that Flink is used? How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm?

What are some use cases that Flink is uniquely qualified to handle?

Where does Flink fit into the current data landscape? How is Flink architected?

How has that architecture evolved? Are there any aspects of the current design that you would do differently if you started over today?

How does scaling work in a Flink deployment?

What are the scaling limits? What are some of the failure modes that users should be aware of?

How is the statefulness of a cluster managed?

What are the mechanisms for managing conflicts? What are the limiting factors for the volume of state that can be practically handled in a cluster and for a given purpose? Can state be shared across processes or tasks within a Flink cluster?

What are the comparative challenges of working with bounded vs unbounded streams of data? How do you handle out of order events in Flink, especially as the delay for a given event increases? For someone who is using Flink in their environment, what are the primary means of interacting with and developing on top of it? What are some of the most challenging or complicated aspects of building and maintaining Flink? What are some of the most interesting or unexpected ways that you have seen Flink used? What are some of the improvements or new features that are planned for the future of Flink? What are some features or use cases that you are explicitly not planning to support? For people who participate in the training sessions that you offer through Data Artisans, what are some of the concepts that they are challenged by?

What do they find most interesting or exciting?

Contact Info

LinkedIn @fhueske on Twitter fhueske on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Flink Data Artisans IBM DB2 Technische Universität Berlin Hadoop Relational Database Google Cloud Dataflow Spark Cascading Java RocksDB Flink Checkpoints Flink Savepoints Kafka Pulsar Storm Scala LINQ (Language INtegrated Query) SQL Backpressure

Summary

Business intelligence is a necessity for any organization that wants to be able to make informed decisions based on the data that they collect. Unfortunately, it is common for different portions of the business to build their reports with different assumptions, leading to conflicting views and poor choices. Looker is a modern tool for building and sharing reports that makes it easy to get everyone on the same page. In this episode Daniel Mintz explains how the product is architected, the features that make it easy for any business user to access and explore their reports, and how you can use it for your organization today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Daniel Mintz about Looker, a a modern data platform that can serve the data needs of an entire company

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Looker is and the problem that it is aiming to solve?

How do you define business intelligence?

How is Looker unique from other approaches to business intelligence in the enterprise?

How does it compare to open source platforms for BI?

Can you describe the technical infrastructure that supports Looker? Given that you are connecting to the customer’s data store, how do you ensure sufficient security? For someone who is using Looker, what does their workflow look like?

How does that change for different user roles (e.g. data engineer vs sales management)

What are the scaling factors for Looker, both in terms of volume of data for reporting from, and for user concurrency? What are the most challenging aspects of building a business intelligence tool and company in the modern data ecosystem?

What are the portions of the Looker architecture that you would do differently if you were to start over today?

What are some of the most interesting or unusual uses of Looker that you have seen? What is in store for the future of Looker?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Looker Upworthy MoveOn.org LookML SQL Business Intelligence Data Warehouse Linux Hadoop BigQuery Snowflake Redshift DB2 PostGres ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Airflow Luigi NiFi Data Curation Episode Presto Hive Athena DRY (Don’t Repeat Yourself) Looker Action Hub Salesforce Marketo Twilio Netscape Navigator Dynamic Pricing Survival Analysis DevOps BigQuery ML Snowflake Data Sharehouse

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Get up to speed with Apache Drill, an extensible distributed SQL query engine that reads massive datasets in many popular file formats such as Parquet, JSON, and CSV. Drill reads data in HDFS or in cloud-native storage such as S3 and works with Hive metastores along with distributed databases such as HBase, MongoDB, and relational databases. Drill works everywhere: on your laptop or in your largest cluster. In this practical book, Drill committers Charles Givre and Paul Rogers show analysts and data scientists how to query and analyze raw data using this powerful tool. Data scientists today spend about 80% of their time just gathering and cleaning data. With this book, you’ll learn how Drill helps you analyze data more effectively to drive down time to insight. Use Drill to clean, prepare, and summarize delimited data for further analysis Query file types including logfiles, Parquet, JSON, and other complex formats Query Hadoop, relational databases, MongoDB, and Kafka with standard SQL Connect to Drill programmatically using a variety of languages Use Drill even with challenging or ambiguous file formats Perform sophisticated analysis by extending Drill’s functionality with user-defined functions Facilitate data analysis for network security, image metadata, and machine learning

Dive into the world of distributed data processing with the 'Apache Hadoop 3 Quick Start Guide.' This comprehensive resource equips you with the knowledge needed to handle large datasets effectively using Apache Hadoop. Learn how to set up and configure Hadoop, work with its core components, and explore its powerful ecosystem tools. What this Book will help me do Understand the fundamental concepts of Apache Hadoop, including HDFS, MapReduce, and YARN, and use them to store and process large datasets. Set up and configure Hadoop 3 in both developer and production environments to suit various deployment needs. Gain hands-on experience with Hadoop ecosystem tools like Hive, Kafka, and Spark to enhance your big data processing capabilities. Learn to manage, monitor, and troubleshoot Hadoop clusters efficiently to ensure smooth operations. Analyze real-time streaming data with tools like Apache Storm and perform advanced data analytics using Apache Spark. Author(s) The author of this guide, Vijay Karambelkar, brings years of experience working with big data technologies and Apache Hadoop in real-world applications. With a passion for teaching and simplifying complex topics, Vijay has compiled his expertise to help learners confidently approach Hadoop 3. His detailed, example-driven approach makes this book a practical resource for aspiring data professionals. Who is it for? This book is ideal for software developers, data engineers, and IT professionals who aspire to dive into the field of big data. If you're new to Apache Hadoop or looking to upgrade your skills to include version 3, this guide is for you. A basic understanding of Java programming is recommended to make the most of the topics covered. Embark on this journey to enhance your career in data-intensive industries.

Summary

With the growth of the Hadoop ecosystem came a proliferation of implementations for the Hive table format. Unfortunately, with no formal specification, each project works slightly different which increases the difficulty of integration across systems. The Hive format is also built with the assumptions of a local filesystem which results in painful edge cases when leveraging cloud object storage for a data lake. In this episode Ryan Blue explains how his work on the Iceberg table format specification and reference implementation has allowed Netflix to improve the performance and simplify operations for their S3 data lake. This is a highly detailed and technical exploration of how a well-engineered metadata layer can improve the speed, accuracy, and utility of large scale, multi-tenant, cloud-native data platforms.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Ryan Blue about Iceberg, a Netflix project to implement a high performance table format for batch workloads

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Iceberg is and the motivation for creating it?

Was the project built with open-source in mind or was it necessary to refactor it from an internal project for public use?

How has the use of Iceberg simplified your work at Netflix? How is the reference implementation architected and how has it evolved since you first began work on it?

What is involved in deploying it to a user’s environment?

For someone who is interested in using Iceberg within their own environments, what is involved in integrating it with their existing query engine?

Is there a migration path for pre-existing tables into the Iceberg format?

How is schema evolution managed at the file level?

How do you handle files on disk that don’t contain all of the fields specified in a table definition?

One of the complicated problems in data modeling is managing table partitions. How does Iceberg help in that regard? What are the unique challenges posed by using S3 as the basis for a data lake?

What are the benefits that outweigh the difficulties?

What have been some of the most challenging or contentious details of the specification to define?

What are some things that you have explicitly left out of the specification?

What are your long-term goals for the Iceberg specification?

Do you anticipate the reference implementation continuing to be used and maintained?

Contact Info

rdblue on GitHub LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Iceberg Reference Implementation Iceberg Table Specification Netflix Hadoop Cloudera Avro Parquet Spark S3 HDFS Hive ORC S3mper Git Metacat Presto Pig DDL (Data Definition Language) Cost-Based Optimization

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Storage systems must provide reliable and convenient data access to all authorized users while simultaneously preventing threats coming from outside or even inside the enterprise. Security threats come in many forms, from unauthorized access to data, data tampering, denial of service, and obtaining privileged access to systems. According to the Storage Network Industry Association (SNIA), data security in the context of storage systems is responsible for safeguarding the data against theft, prevention of unauthorized disclosure of data, prevention of data tampering, and accidental corruption. This process ensures accountability, authenticity, business continuity, and regulatory compliance. Security for storage systems can be classified as follows: Data storage (data at rest, which includes data durability and immutability) Access to data Movement of data (data in flight) Management of data IBM® Spectrum Scale is a software-defined storage system for high performance, large-scale workloads on-premises or in the cloud. IBM Spectrum™ Scale addresses all four aspects of security by securing data at rest (protecting data at rest with snapshots, and backups and immutability features) and securing data in flight (providing secure management of data, and secure access to data by using authentication and authorization across multiple supported access protocols). These protocols include POSIX, NFS, SMB, Hadoop, and Object (REST). For automated data management, it is equipped with powerful information lifecycle management (ILM) tools that can help administer unstructured data by providing the correct security for the correct data. This IBM Redpaper™ publication details the various aspects of security in IBM Spectrum Scale™, including the following items: Security of data in transit Security of data at rest Authentication Authorization Hadoop security Immutability Secure administration Audit logging Security for transparent cloud tiering (TCT) Security for OpenStack drivers Unless stated otherwise, the functions that are mentioned in this paper are available in IBM Spectrum Scale V4.2.1 or later releases.

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?

Cleanliness/accuracy

What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides?

How has that architecture evolved from when you first started? What would you do differently if you were to start over today?

Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Alex

@alexcrdean on Twitter LinkedIn

Snowplow

@snowplowdata on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Snowplow

GitHub

Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics

Podcast Interview

Redshift SnowflakeDB Snowplow Insights Googl

Summary

There are myriad reasons why data should be protected, and just as many ways to enforce it in tranist or at rest. Unfortunately, there is still a weak point where attackers can gain access to your unencrypted information. In this episode Ellison Anny Williams, CEO of Enveil, describes how her company uses homomorphic encryption to ensure that your analytical queries can be executed without ever having to decrypt your data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Ellison Anne Williams about Enveil, a pioneering data security company protecting Data in Use

Interview

Introduction How did you get involved in the area of data security? Can you start by explaining what your mission is with Enveil and how the company got started? One of the core aspects of your platform is the principal of homomorphic encryption. Can you explain what that is and how you are using it?

What are some of the challenges associated with scaling homomorphic encryption? What are some difficulties associated with working on encrypted data sets?

Can you describe the underlying architecture for your data platform?

How has that architecture evolved from when you first began building it?

What are some use cases that are unlocked by having a fully encrypted data platform? For someone using the Enveil platform, what does their workflow look like? A major reason for never decrypting data is to protect it from attackers and unauthorized access. What are some of the remaining attack vectors? What are some aspects of the data being protected that still require additional consideration to prevent leaking information? (e.g. identifying individuals based on geographic data, or purchase patterns) What do you have planned for the future of Enveil?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data security today?

Links

Enveil NSA GDPR Intellectual Property Zero Trust Homomorphic Encryption Ciphertext Hadoop PII (Personally Identifiable Information) TLS (Transport Layer Security) Spark Elasticsearch Side-channel attacks Spectre and Meltdown

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications. What You Will Learn Understand Spark unified data processing platform Howto run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functions Build real-time applications using Spark Structured Streaming Develop intelligent applications with the Spark Machine Learning library Who This Book Is For Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines. Presents new methodologies that are widely applicable to just about any project involving large and complex datasets Offers readers informative new case studies across a range scientific and engineering disciplines Provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues Utilizes a combination of pseudocode and very short snippets of Python code to show readers how they may develop their own projects without downloading or learning new software

Fast data ingestion, serving, and analytics in the Hadoop ecosystem have forced developers and architects to choose solutions using the least common denominator—either fast analytics at the cost of slow data ingestion or fast data ingestion at the cost of slow analytics. There is an answer to this problem. With the Apache Kudu column-oriented data store, you can easily perform fast analytics on fast data. This practical guide shows you how. Begun as an internal project at Cloudera, Kudu is an open source solution compatible with many data processing frameworks in the Hadoop environment. In this book, current and former solutions professionals from Cloudera provide use cases, examples, best practices, and sample code to help you get up to speed with Kudu. Explore Kudu’s high-level design, including how it spreads data across servers Fully administer a Kudu cluster, enable security, and add or remove nodes Learn Kudu’s client-side APIs, including how to integrate Apache Impala, Spark, and other frameworks for data manipulation Examine Kudu’s schema design, including basic concepts and primitives necessary to make your project successful Explore case studies for using Kudu for real-time IoT analytics, predictive modeling, and in combination with another storage engine

"Apache Hive Essentials" provides a focused guide to mastering the essential techniques of processing and analyzing big data with Apache Hive. What this Book will help me do Set up and configure a Hive environment for big data analysis. Compose effective queries using Hive's SQL-like language to extract insights. Optimize Hive performance to handle complex datasets efficiently. Implement data security and user-defined functions to extend capabilities. Integrate Hive with Hadoop tools for comprehensive data solutions. Author(s) Dayong Du, the author of "Apache Hive Essentials," has years of experience working with big data technologies and tools. With hands-on expertise in Hadoop and the entire ecosystem, he brings a practical and informed perspective to this complex field. His approach is to make these technologies accessible to developers and analysts of all levels. Who is it for? This book is perfect for data analysts, developers, or professionals familiar with SQL who are looking to start with Apache Hive for big data processing. It is suitable for those acquainted with Hadoop and its environment and want to expand their skills into efficient data querying and management. Readers should have an interest in how to leverage big data tools for real-world solutions.