Use MySQL Shell, the first modern and advanced client for connecting to and interacting with MySQL. It supports SQL, Python, and JavaScript. That’s right! You can write Python scripts and execute them within the shell interactively, or in batch mode. The level of automation available from Python combined with batch mode is especially helpful to those practicing DevOps methods in their database environments. Introducing MySQL Shell covers everything you need to know about MySQL Shell. You will learn how to use the shell for SQL, as well as the new application programming interfaces for working with a document store and even automating your management of MySQL servers using Python. The book includes a look at the supporting technologies and concepts such as JSON, schema-less documents, NoSQL, MySQL Replication, Group Replication, InnoDB Cluster, and more. MySQL Shell is the client that developers and databaseadministrators have been waiting for. Far more powerful than the legacy client, MySQL Shell enables levels of automation that are useful not only for MySQL, but in the broader context of your career as well. Automate your work and build skills in one of the most in-demand languages. With MySQL Shell, you can do both! What You'll Learn Use MySQL Shell with the newest features in MySQL 8 Discover what a Document Store is and how to manage it with MySQL Shell Configure Group Replication and InnoDB Cluster from MySQL Shell Understand the new MySQL Python application programming interfaces Write Python scripts for managing your data and the MySQL high availability features Who This Book Is For Developers and database professionals who want to automate their work and remain on the cutting edge of what MySQLhas to offer. Anyone not happy with the limited automation capabilities of the legacy command-line client will find much to like in this book on the MySQL Shell that supports powerful automation through the Python scripting language.

"Big Data Simplified blends technology with strategy and delves into applications of big data in specialized areas, such as recommendation engines, data science and Internet of Things (IoT) and enables a practitioner to make the right technology choice. The steps to strategize a big data implementation are also discussed in detail. This book presents a holistic approach to the topic, covering a wide landscape of big

data technologies like Hadoop 2.0 and package implementations, such as Cloudera. In-depth discussion of associated technologies, such as MapReduce, Hive, Pig, Oozie, ApacheZookeeper, Flume, Kafka, Spark, Python and NoSQL databases like Cassandra, MongoDB, GraphDB, etc., is also included.

Summary One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale with your business.

Announcements

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 our friends at 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. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. 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 and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Evan Weaver about FaunaDB, a modern operational data platform built for your cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what FaunaDB is and how it got started? What are some of the main use cases that FaunaDB is targeting?

How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?

Can you describe the architecture of FaunaDB and how it has evolved? The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?

What are some of the edge cases that users should be aware of? How are conflicts managed in Fauna?

What is the underlying storage layer?

How is the query layer designed to allow for different query patterns and model representations?

How does data modeling in Fauna compare to that of relational or document databases?

Can you describe the query format? What are some of the common difficulties or points of confusion around interacting with data in Fauna?

What are some application design patterns that are enabled by using Fauna as the storage layer? Given the ability to replicate globally, how do you mitigate latency when interacting with the database? What are some of the most interesting or unexpected ways that you have seen Fauna used? When is it the wrong choice? What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company? What do you have in store for the future of Fauna?

Contact Info

@evan on Twitter LinkedIn

Parting Question

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

Links

Fauna Ruby on Rails CNET GitHub Twitter NoSQL Cassandra InnoDB Redis Memcached Timeseries Spanner Paper DynamoDB Paper Percolator ACID Calvin Protocol Daniel Abadi LINQ LSM Tree (Log-structured Merge-tree) Scala Change Data Capture GraphQL

Podcast.init Interview About Graphene

Fauna Query Language (FQL) CQL == Cassandra Query Language Object-Relational Databases LDAP == Lightweight Directory Access Protocol Auth0 OLAP == Online Analytical Processing Jepsen distributed systems safety research

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

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Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code. PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You’ll also discover how to solve problems in graph analysis using graphframes. On completing this book, you’ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases. What You Will Learn Understand PySpark SQL and its advanced features Use SQL and HiveQL with PySpark SQL Work with structured streaming Optimize PySpark SQL Master graphframes and graph processing Who This Book Is For Data scientists, Python programmers, and SQL programmers.

"MongoDB 4 Quick Start Guide" is your gateway into understanding and utilizing MongoDB, the world's leading NoSQL database alternative. Through this approachable guide, you will quickly learn how to install, secure, and effectively perform database operations using MongoDB Version 4. What this Book will help me do Master the installation and configuration of MongoDB to prepare for secure database setups. Execute CRUD operations seamlessly to manage your data through the MongoDB shell. Construct queries using the aggregation pipeline for robust data analysis. Implement replication and sharding to ensure data safety and scaleability. Use the PHP MongoDB driver to integrate MongoDB effectively with web applications. Author(s) None Bierer is an expert in database technologies with extensive experience in NoSQL solutions, particularly MongoDB. Their passion for teaching developers new and efficient ways to work with databases shines through in this practical and hands-on guide. Who is it for? This book is perfect for web developers looking to enhance their understanding of modern databases, IT professionals interested in NoSQL solutions, and DBAs transitioning from relational databases to document-oriented databases. Prior experience with databases can be helpful, but this guide is accessible even for enthusiastic beginners seeking to learn MongoDB.

Learn Azure Cosmos DB and its MongoDB API with hands-on samples and advanced features such as the multi-homing API, geo-replication, custom indexing, TTL, request units (RU), consistency levels, partitioning, and much more. Each chapter explains Azure Cosmos DB’s features and functionalities by comparing it to MongoDB with coding samples. Cosmos DB for MongoDB Developers starts with an overview of NoSQL and Azure Cosmos DB and moves on to demonstrate the difference between geo-replication of Azure Cosmos DB compared to MongoDB. Along the way you’ll cover subjects including indexing, partitioning, consistency, and sizing, all of which will help you understand the concepts of read units and how this calculation is derived from an existing MongoDB’s usage. The next part of the book shows you the process and strategies for migrating to Azure Cosmos DB. You will learn the day-to-day scenarios of using Azure Cosmos DB, its sizing strategies, and optimizing techniques for the MongoDB API. This information will help you when planning to migrate from MongoDB or if you would like to compare MongoDB to the Azure Cosmos DB MongoDB API before considering the switch. What You Will Learn Migrate to MongoDB and understand its strategies Develop a sample application using MongoDB’s client driver Make use of sizing best practices and performance optimization scenarios Optimize MongoDB’s partition mechanism and indexing Who This Book Is For MongoDB developers who wish to learn Azure Cosmos DB. It specifically caters to a technical audience, working on MongoDB.

Learn the new Document Store feature of MySQL 8 and build applications around a mix of the best features from SQL and NoSQL database paradigms. Don’t allow yourself to be forced into one paradigm or the other, but combine both approaches by using the Document Store. MySQL 8 was designed from the beginning to bridge the gap between NoSQL and SQL. Oracle recognizes that many solutions need the capabilities of both. More specifically, developers need to store objects as loose collections of schema-less documents, but those same developers also need the ability to run structured queries on their data. With MySQL 8, you can do both! Introducing the MySQL 8 Document Store presents new tools and features that make creating a hybrid database solution far easier than ever before. This book covers the vitally important MySQL Document Store, the new X Protocol for developing applications, and a new client shell called the MySQL Shell. Also covered are supporting technologies and concepts such as JSON, schema-less documents, and more. The book gives insight into how features work and how to apply them to get the most out of your MySQL experience. The book covers topics such as: The headline feature in MySQL 8 MySQL's answer to NoSQL New APIs and client protocols What You'll Learn Create NoSQL-style applications by using the Document Store Mix the NoSQL and SQL approaches by using each to its best advantage in a hybrid solution Work with the new X Protocol for application connectivity in MySQL 8 Master the new X Developer Application Programming Interfaces Combine SQL and JSON in the same database and application Migrate existing applications to MySQL Document Store Who This Book Is For Developers and database professionals wanting to learn about the most profound paradigm-changing features of the MySQL 8 Document Store

Summary

With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments 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. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Peter Mattis about CockroachDB, the SQL database for global cloud services

Interview

Introduction How did you get involved in the area of data management? What was the motivation for creating CockroachDB and building a business around it? Can you describe the architecture of CockroachDB and how it supports distributed ACID transactions?

What are some of the tradeoffs that are necessary to allow for georeplicated data with distributed transactions? What are some of the problems that you have had to work around in the RAFT protocol to provide reliable operation of the clustering mechanism?

Go is an unconventional language for building a database. What are the pros and cons of that choice? What are some of the common points of confusion that users of CockroachDB have when operating or interacting with it?

What are the edge cases and failure modes that users should be aware of?

I know that your SQL syntax is PostGreSQL compatible, so is it possible to use existing ORMs unmodified with CockroachDB?

What are some examples of extensions that are specific to CockroachDB?

What are some of the most interesting uses of CockroachDB that you have seen? When is CockroachDB the wrong choice? What do you have planned for the future of CockroachDB?

Contact Info

Peter

LinkedIn petermattis on GitHub @petermattis on Twitter

Cockroach Labs

@CockroackDB on Twitter Website cockroachdb on GitHub

Parting Question

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

Links

CockroachDB Cockroach Labs SQL Google Bigtable Spanner NoSQL RDBMS (Relational Database Management System) “Big Iron” (colloquial term for mainframe computers) RAFT Consensus Algorithm Consensus MVCC (Multiversion Concurrency Control) Isolation Etcd GDPR Golang C++ Garbage Collection Metaprogramming Rust Static Linking Docker Kubernetes CAP Theorem PostGreSQL ORM (Object Relational Mapping) Information Schema PG Catalog Interleaved Tables Vertica Spark Change Data Capture

The intro and outro music is from The Hug by The Freak Fandan

Spark for Data Professionals introduces and solidifies the concepts behind Spark 2.x, teaching working developers, architects, and data professionals exactly how to build practical Spark solutions. Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR, Spark Streaming, Messaging, NoSQL and Hadoop integration. Each chapter presents practical exercises deploying Spark to your local or cloud environment, plus programming exercises for building real applications. Unlike other Spark guides, Spark for Data Professionals explains crucial concepts step-by-step, assuming no extensive background as an open source developer. It provides a complete foundation for quickly progressing to more advanced data science and machine learning topics. This guide will help you: Understand Spark basics that will make you a better programmer and cluster “citizen” Master Spark programming techniques that maximize your productivity Choose the right approach for each problem Make the most of built-in platform constructs, including broadcast variables, accumulators, effective partitioning, caching, and checkpointing Leverage powerful tools for managing streaming, structured, semi-structured, and unstructured data

A Deep Dive into NoSQL Databases: The Use Cases and Applications, Volume 109, the latest release in the Advances in Computers series first published in 1960, presents detailed coverage of innovations in computer hardware, software, theory, design and applications. In addition, it provides contributors with a medium in which they can explore their subjects in greater depth and breadth. This update includes sections on NoSQL and NewSQL databases for big data analytics and distributed computing, NewSQL databases and scalable in-memory analytics, NoSQL web crawler application, NoSQL Security, a Comparative Study of different In-Memory (No/New)SQL Databases, NoSQL Hands On-4 NoSQLs, the Hadoop Ecosystem, and more. Provides a very comprehensive, yet compact, book on the popular domain of NoSQL databases for IT professionals, practitioners and professors Articulates and accentuates big data analytics and how it gets simplified and streamlined by NoSQL database systems Sets a stimulating foundation with all the relevant details for NoSQL database researchers, developers and administrators

In this podcast, Wayne Eckerson and James Serra discuss myths of modern data management. Some of the myths discussed include 'all you need is a data lake', 'the data warehouse is dead', 'we don’t need OLAP cubes anymore', 'cloud is too expensive and latency is too slow', 'you should always use a NoSQL product over a RDBMS.'

Serra is big data and data warehousing solutions architect at Microsoft with over thirty years of IT experience. He is a popular blogger and speaker and has presented at dozens of Microsoft PASS and other events. Prior to Microsoft, Serra was an independent data warehousing and business intelligence architect and developer.

Learn the fundamentals of seven essential NoSQL databases in just one week with this book. Covering MongoDB, DynamoDB, Redis, Cassandra, Neo4j, InfluxDB, and HBase, you'll explore their functionalities and practical applications. Designed to give you a working understanding of NoSQL database types, this guide helps aspiring DBAs and developers comprehend and utilize modern data solutions. What this Book will help me do Master the fundamentals of MongoDB, including high-performance, high-availability, and scaling features. Gain hands-on experience with Neo4j to perform database queries and integrate with Python and Java applications. Learn efficient querying with Redis for storage and retrieval tasks. Understand Cassandra's powerful solution for scalable and fault-tolerant systems. Get well-versed with HBase for creating tables, and reading and writing data efficiently. Author(s) Sudarshan Kadambi and Xun (Brian) Wu bring a wealth of experience in database technologies. They have worked extensively in the software development and database management fields. With their practical and concise teaching approach, the authors make complex topics accessible for readers. Who is it for? This book is ideal for budding DBAs and developers looking to understand NoSQL databases. It is particularly useful for those transitioning from relational databases who want to learn about modern database technologies. Suitable for both beginners and those with some database knowledge, it aims to bridge skill gaps and expand the reader's technical expertise.

Summary

Search is a common requirement for applications of all varieties. Elasticsearch was built to make it easy to include search functionality in projects built in any language. From that foundation, the rest of the Elastic Stack has been built, expanding to many more use cases in the proces. In this episode Philipp Krenn describes the various pieces of the stack, how they fit together, and how you can use them in your infrastructure to store, search, and analyze 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. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Philipp Krenn about the Elastic Stack and the ways that you can use it in your systems

Interview

Introduction How did you get involved in the area of data management? The Elasticsearch product has been around for a long time and is widely known, but can you give a brief overview of the other components that make up the Elastic Stack and how they work together? Beyond the common pattern of using Elasticsearch as a search engine connected to a web application, what are some of the other use cases for the various pieces of the stack? What are the common scaling bottlenecks that users should be aware of when they are dealing with large volumes of data? What do you consider to be the biggest competition to the Elastic Stack as you expand the capabilities and target usage patterns? What are the biggest challenges that you are tackling in the Elastic stack, technical or otherwise? What are the biggest challenges facing Elastic as a company in the near to medium term? Open source as a business model: https://www.elastic.co/blog/doubling-down-on-open?utm_source=rss&utm_medium=rss What is the vision for Elastic and the Elastic Stack going forward and what new features or functionality can we look forward to?

Contact Info

@xeraa on Twitter xeraa on GitHub Website Email

Parting Question

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

Links

Elastic Vienna – Capital of Austria What Is Developer Advocacy? NoSQL MongoDB Elasticsearch Cassandra Neo4J Hazelcast Apache Lucene Logstash Kibana Beats X-Pack ELK Stack Metrics APM (Application Performance Monitoring) GeoJSON Split Brain Elasticsearch Ingest Nodes PacketBeat Elastic Cloud Elasticon Kibana Canvas SwiftType

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Summary

As software lifecycles move faster, the database needs to be able to keep up. Practices such as version controlled migration scripts and iterative schema evolution provide the necessary mechanisms to ensure that your data layer is as agile as your application. Pramod Sadalage saw the need for these capabilities during the early days of the introduction of modern development practices and co-authored a book to codify a large number of patterns to aid practitioners, and in this episode he reflects on the current state of affairs and how things have changed over the past 12 years.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Pramod Sadalage about refactoring databases and integrating database design into an iterative development workflow

Interview

Introduction How did you get involved in the area of data management? You first co-authored Refactoring Databases in 2006. What was the state of software and database system development at the time and why did you find it necessary to write a book on this subject? What are the characteristics of a database that make them more difficult to manage in an iterative context? How does the practice of refactoring in the context of a database compare to that of software? How has the prevalence of data abstractions such as ORMs or ODMs impacted the practice of schema design and evolution? Is there a difference in strategy when refactoring the data layer of a system when using a non-relational storage system? How has the DevOps movement and the increased focus on automation affected the state of the art in database versioning and evolution? What have you found to be the most problematic aspects of databases when trying to evolve the functionality of a system? Looking back over the past 12 years, what has changed in the areas of database design and evolution?

How has the landscape of tooling for managing and applying database versioning changed since you first wrote Refactoring Databases? What do you see as the biggest challenges facing us over the next few years?

Contact Info

Website pramodsadalage on GitHub @pramodsadalage on Twitter

Parting Question

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

Links

Database Refactoring

Website Book

Thoughtworks Martin Fowler Agile Software Development XP (Extreme Programming) Continuous Integration

The Book Wikipedia

Test First Development DDL (Data Definition Language) DML (Data Modification Language) DevOps Flyway Liquibase DBMaintain Hibernate SQLAlchemy ORM (Object Relational Mapper) ODM (Object Document Mapper) NoSQL Document Database MongoDB OrientDB CouchBase CassandraDB Neo4j ArangoDB Unit Testing Integration Testing OLAP (On-Line Analytical Processing) OLTP (On-Line Transaction Processing) Data Warehouse Docker QA==Quality Assurance HIPAA (Health Insurance Portability and Accountability Act) PCI DSS (Payment Card Industry Data Security Standard) Polyglot Persistence Toplink Java ORM Ruby on Rails ActiveRecord Gem

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

Summary

As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Timescale is and how the project got started? The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options? In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices? How is Timescale implemented and how has the internal architecture evolved since you first started working on it?

What impact has the 10.0 release of PostGreSQL had on the design of the project? Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL?

For someone who wants to start using Timescale what is involved in deploying and maintaining it? What are the axes for scaling Timescale and what are the points where that scalability breaks down?

Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances?

What has been the most challenging aspect of building and marketing Timescale? When is Timescale the wrong tool to use for time series data? One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus? What are some of the most interesting uses of Timescale that you have seen? Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health? What features or improvements do you have planned for future releases of Timescale?

Contact Info

Ajay

LinkedIn @acoustik on Twitter Timescale Blog

Mike

Website LinkedIn @michaelfreedman on Twitter Timescale Blog

Timescale

Website @timescaledb on Twitter GitHub

Parting Question

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

Links

Timescale PostGreSQL Citus Timescale Design Blog Post MIT NYU Stanford SDN Princeton Machine Data Timeseries Data List of Timeseries Databases NoSQL Online Transaction Processing (OLTP) Object Relational Mapper (ORM) Grafana Tableau Kafka When Boring Is Awesome PostGreSQL RDS Google Cloud SQL Azure DB Docker Continuous Aggregates Streaming Replication PGPool II Kubernetes Docker Swarm Citus Data

Website Data Engineering Podcast Interview

Database Indexing B-Tree Index GIN Index GIST Index STE Energy Redis Graphite Prometheus pg_prometheus OpenMetrics Standard Proposal Timescale Parallel Copy Hadoop PostGIS KDB+ DevOps Internet of Things MongoDB Elastic DataBricks Apache Spark Confluent New Enterprise Associates MapD Benchmark Ventures Hortonworks 2σ Ventures CockroachDB Cloudflare EMC Timescale Blog: Why SQL is beating NoSQL, and what this means for the future of data

The intro and outro music is from a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&utm_medium=rss" target="_blank"…

Practical Big Data Analytics is your ultimate guide to harnessing Big Data technologies for enterprise analytics and machine learning. By leveraging tools like Hadoop, Spark, NoSQL databases, and frameworks such as R, this book equips you with the skills to implement robust data solutions that drive impactful business insights. Gain practical expertise in handling data at scale and uncover the value behind the numbers. What this Book will help me do Master the fundamental concepts of Big Data storage, processing, and analytics. Gain practical skills in using tools like Hadoop, Spark, and NoSQL databases for large-scale data handling. Develop and deploy machine learning models and dashboards with R and R Shiny. Learn strategies for creating cost-efficient and scalable enterprise data analytics solutions. Understand and implement effective approaches to combining Big Data technologies for actionable insights. Author(s) None Dasgupta is an expert in Big Data analytics, statistical methodologies, and enterprise data solutions. With years of experience consulting on enterprise data platforms and working with leading industry technologies, Dasgupta brings a wealth of practical knowledge to help readers navigate and succeed in the field of Big Data. Through this book, Dasgupta shares an accessible and systematic way to learn and apply key Big Data concepts. Who is it for? This book is ideal for professionals eager to delve into Big Data analytics, regardless of their current level of expertise. It accommodates both aspiring analysts and seasoned IT professionals looking to enhance their knowledge in data-driven decision making. Individuals with a technical inclination and a drive to build Big Data architectures will find this book particularly beneficial. No prior knowledge of Big Data is required, although familiarity with programming concepts will enhance the learning experience.

Summary

PostGreSQL has become one of the most popular and widely used databases, and for good reason. The level of extensibility that it supports has allowed it to be used in virtually every environment. At Citus Data they have built an extension to support running it in a distributed fashion across large volumes of data with parallelized queries for improved performance. In this episode Ozgun Erdogan, the CTO of Citus, and Craig Kerstiens, Citus Product Manager, discuss how the company got started, the work that they are doing to scale out PostGreSQL, and how you can start using it in your environment.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Ozgun Erdogan and Craig Kerstiens about Citus, worry free PostGreSQL

Interview

Introduction How did you get involved in the area of data management? Can you describe what Citus is and how the project got started? Why did you start with Postgres vs. building something from the ground up? What was the reasoning behind converting Citus from a fork of PostGres to being an extension and releasing an open source version? How well does Citus work with other Postgres extensions, such as PostGIS, PipelineDB, or Timescale? How does Citus compare to options such as PostGres-XL or the Postgres compatible Aurora service from Amazon? How does Citus operate under the covers to enable clustering and replication across multiple hosts? What are the failure modes of Citus and how does it handle loss of nodes in the cluster? For someone who is interested in migrating to Citus, what is involved in getting it deployed and moving the data out of an existing system? How do the different options for leveraging Citus compare to each other and how do you determine which features to release or withhold in the open source version? Are there any use cases that Citus enables which would be impractical to attempt in native Postgres? What have been some of the most challenging aspects of building the Citus extension? What are the situations where you would advise against using Citus? What are some of the most interesting or impressive uses of Citus that you have seen? What are some of the features that you have planned for future releases of Citus?

Contact Info

Citus Data

citusdata.com @citusdata on Twitter citusdata on GitHub

Craig

Email Website @craigkerstiens on Twitter

Ozgun

Email ozgune on GitHub

Parting Question

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

Links

Citus Data PostGreSQL NoSQL Timescale SQL blog post PostGIS PostGreSQL Graph Database JSONB Data Type PipelineDB Timescale PostGres-XL Aurora PostGres Amazon RDS Streaming Replication CitusMX CTE (Common Table Expression) HipMunk Citus Sharding Blog Post Wal-e Wal-g Heap Analytics HyperLogLog C-Store

The intro and outro musi

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