Big Data

Analyze Massive Data At Interactive Speeds With The Power Of Bitmaps Using FeatureBase - Episode 345

The most expensive part of working with massive data sets is the work of retrieving and processing the files that contain the raw information. FeatureBase (formerly Pilosa) avoids that overhead by converting the data into bitmaps. In this episode Matt Jaffee explains how to model your data as bitmaps and the benefits that this representation provides for fast aggregate computation. He also discusses the improvements that have been incorporated into FeatureBase to simplify integration with the rest of your data stack, and the SQL interface that was added to make working with the product easier.

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Interactive Exploratory Data Analysis On Petabyte Scale Data Sets With Arkouda - Episode 311

Exploratory data analysis works best when the feedback loop is fast and iterative. This is easy to achieve when you are working on small datasets, but as they scale up beyond what can fit on a single machine those short iterations quickly become long and tedious. The Arkouda project is a Python interface built on top of the Chapel compiler to bring back those interactive speeds for exploratory analysis on horizontally scalable compute that parallelizes operations on large volumes of data. In this episode David Bader explains how the framework operates, the algorithms that are built into it to support complex analyses, and how you can start using it today.

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How And Why To Become Data Driven As A Business - Episode 229

Organizations of all sizes are striving to become data driven, starting in earnest with the rise of big data a decade ago. With the never-ending growth in data sources and methods for aggregating and analyzing them, the use of data to direct the business has become a requirement. Randy Bean has been helping enterprise organizations define and execute their data strategies since before the age of big data. In this episode he discusses his experiences and how he approached the work of distilling them for his book “Fail Fast, Learn Faster”. This is an entertaining and enlightening exploration of the business side of data with an industry veteran.

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Charting A Path For Streaming Data To Fill Your Data Lake With Hudi - Episode 209

Data lake architectures have largely been biased toward batch processing workflows due to the volume of data that they are designed for. With more real-time requirements and the increasing use of streaming data there has been a struggle to merge fast, incremental updates with large, historical analysis. Vinoth Chandar helped to create the Hudi project while at Uber to address this challenge. By adding support for small, incremental inserts into large table structures, and building support for arbitrary update and delete operations the Hudi project brings the best of both worlds together. In this episode Vinoth shares the history of the project, how its architecture allows for building more frequently updated analytical queries, and the work being done to add a more polished experience to the data lake paradigm.

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Proven Patterns For Building Successful Data Teams - Episode 162

Building data products are complicated by the fact that there are so many different stakeholders with competing goals and priorities. It is also challenging because of the number of roles and capabilities that are necessary to go from idea to delivery. Different organizations have tried a multitude of organizational strategies to improve the success rate of these data teams with varying levels of success. In this episode Jesse Anderson shares the lessons that he has learned while working with dozens of businesses across industries to determine the team structures and communication styles that have generated the best results. If you are struggling to deliver value from big data, or just starting down the path of building the organizational capacity to turn raw information into valuable products then this is a conversation that you don’t want to miss.

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Building A Better Data Warehouse For The Cloud At Firebolt - Episode 148

Data warehouse technology has been around for decades and has gone through several generational shifts in that time. The current trends in data warehousing are oriented around cloud native architectures that take advantage of dynamic scaling and the separation of compute and storage. Firebolt is taking that a step further with a core focus on speed and interactivity. In this episode CEO and founder Eldad Farkash explains how the Firebolt platform is architected for high throughput, their simple and transparent pricing model to encourage widespread use, and the use cases that it unlocks through interactive query speeds.

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Exploring The TileDB Universal Data Engine - Episode 146

Most databases are designed to work with textual data, with some special purpose engines that support domain specific formats. TileDB is a data engine that was built to support every type of data by using multi-dimensional arrays as the foundational primitive. In this episode the creator and founder of TileDB shares how he first started working on the underlying technology and the benefits of using a single engine for efficiently storing and querying any form of data. He also discusses the shifts in database architectures from vertically integrated monoliths to separately deployed layers, and the approach he is taking with TileDB cloud to embed the authorization into the storage engine, while providing a flexible interface for compute. This was a great conversation about a different approach to database architecture and how that enables a more flexible way to store and interact with data to power better data sharing and new opportunities for blending specialized domains.

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A High Performance Platform For The Full Big Data Lifecycle - Episode 94

Managing big data projects at scale is a perennial problem, with a wide variety of solutions that have evolved over the past 20 years. One of the early entrants that predates Hadoop and has since been open sourced is the HPCC (High Performance Computing Cluster) system. Designed as a fully integrated platform to meet the needs of enterprise grade analytics it provides a solution for the full lifecycle of data at massive scale. In this episode Flavio Villanustre, VP of infrastructure and products at HPCC Systems, shares the history of the platform, how it is architected for scale and speed, and the unique solutions that it provides for enterprise grade data analytics. He also discusses the motivations for open sourcing the platform, the detailed workflow that it enables, and how you can try it for your own projects. This was an interesting view of how a well engineered product can survive massive evolutionary shifts in the industry while remaining relevant and useful.

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Straining Your Data Lake Through A Data Mesh - Episode 90

The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access. In this episode Zhamak Dehghani shares an alternative approach in the form of a data mesh. Rather than connecting all of your data flows to one destination, empower your individual business units to create data products that can be consumed by other teams. This was an interesting exploration of a different way to think about the relationship between how your data is produced, how it is used, and how to build a technical platform that supports the organizational needs of your business.

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Data Labeling That You Can Feel Good About With CloudFactory - Episode 89

Successful machine learning and artificial intelligence projects require large volumes of data that is properly labelled. The challenge is that most data is not clean and well annotated, requiring a scalable data labeling process. Ideally this process can be done using the tools and systems that already power your analytics, rather than sending data into a black box. In this episode Mark Sears, CEO of CloudFactory, explains how he and his team built a platform that provides valuable service to businesses and meaningful work to developing nations. He shares the lessons learned in the early years of growing the business, the strategies that have allowed them to scale and train their workforce, and the benefits of working within their customer’s existing platforms. He also shares some valuable insights into the current state of the art for machine learning in the real world.

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