Cloudera Engineering Blog · Hadoop Posts

Index-Level Security Comes to Cloudera Search

The integration of Apache Sentry with Apache Solr helps Cloudera Search meet important security requirements.

As you have learned in previous blog posts, Cloudera Search brings the power of Apache Hadoop to a wide variety of business users via the ease and flexibility of full-text querying provided by Apache Solr. We have also done significant work to make Cloudera Search easy to add to an existing Hadoop cluster:

The Truth About MapReduce Performance on SSDs

Cost-per-performance, not cost-per-capacity, turns out to be the better metric for evaluating the true value of SSDs.

In the Big Data ecosystem, solid-state drives (SSDs) are increasingly considered a viable, higher-performance alternative to rotational hard-disk drives (HDDs). However, few results from actual testing are available to the public.

This Month in the Ecosystem (February 2014)

Welcome to our sixth edition of “This Month in the Ecosystem,” a digest of highlights from February 2014 (never intended to be comprehensive; for completeness, see the excellent Hadoop Weekly).

February being a short month, the list is relatively short — but never confuse quantity with quality!

A Guide to Checkpointing in Hadoop

Understanding how checkpointing works in HDFS can make the difference between a healthy cluster or a failing one.

Checkpointing is an essential part of maintaining and persisting filesystem metadata in HDFS. It’s crucial for efficient NameNode recovery and restart, and is an important indicator of overall cluster health. However, checkpointing can also be a source of confusion for operators of Apache Hadoop clusters.

Apache Hadoop 2.3.0 is Released (HDFS Caching FTW!)

Hadoop 2.3.0 includes hundreds of new fixes and features, but none more important than HDFS caching.

The Apache Hadoop community has voted to release Hadoop 2.3.0, which includes (among many other things):

How-to: Make Hadoop Accessible via LDAP

Integrating Hue with LDAP can help make your secure Hadoop apps as widely consumed as possible.

Hue, the open source Web UI that makes Apache Hadoop easier to use, easily integrates with your corporation’s existing identity management systems and provides authentication mechanisms for SSO providers. So, by changing a few configuration parameters, your employees can start analyzing Big Data in their own browsers under an existing security policy.

Getting MapReduce 2 Up to Speed

Thanks to the improvements described here, CDH 5 will ship with a version of MapReduce 2 that is just as fast (or faster) than MapReduce 1.

Performance fixes are tiny, easy, and boring, once you know what the problem is. The hard work is in putting your finger on that problem: narrowing, drilling down, and measuring, measuring, measuring.

Cloudera Enterprise 5 Beta 2 is Available: More New Features and Components

Cloudera has released the Beta 2 version of Cloudera Enterprise 5 (comprises CDH 5.0.0 and Cloudera Manager 5.0.0). 

This release (download) contains a number of new features and component versions including the ones below:

This Month in the Ecosystem (January 2014)

Welcome to our fifth edition of “This Month in the Ecosystem,” a digest of highlights from January 2014 (never intended to be comprehensive; for completeness, see the excellent Hadoop Weekly).

With the close of 2013, we also thought it appropriate to include some high points from across the year (not listed in any particular order):

How-to: Write and Run Apache Giraph Jobs on Apache Hadoop

Create a test environment for writing and testing Giraph jobs, or just for playing around with Giraph and small sample datasets.

Apache Giraph is a scalable, fault-tolerant implementation of graph-processing algorithms in Apache Hadoop clusters of up to thousands of computing nodes. Giraph is in use at companies like Facebook and PayPal, for example, to help represent and analyze the billions (or even trillions) of connections across massive datasets. Giraph was inspired by Google’s Pregel framework and integrates well with Apache Accumulo, Apache HBase, Apache Hive, and Cloudera Impala.

How-to: Create a Simple Hadoop Cluster with VirtualBox

Set up a CDH-based Hadoop cluster in less than an hour using VirtualBox and Cloudera Manager.

Thanks to Christian Javet for his permission to republish his blog post below!

NYU, Analytics, and Cloudera’s QuickStart VM

The Cloudera QuickStart VM is an important platform for learning any Hadoop-related curriculum.

In the Fall 2013 semester, more than 30 NYU graduate students completed the Real-time and Big Data Analytics course at the NYU Courant Institute of Mathematical Sciences, for which I served as instructor.

This Month (and Year) in the Ecosystem (December 2013)

Welcome to our sixth edition of “This Month in the Ecosystem,” a digest of highlights from December 2013 (never intended to be comprehensive; for completeness, see the excellent Hadoop Weekly).

With the close of 2013, we also thought it appropriate to include some high points from across the year (not listed in any particular order):

The Cloudera Developer Newsletter: It’s For You!

The new Cloudera Developer Newsletter makes its debut in January 2014.

Developers and data scientists, we’re realize you’re special – as are operators and analysts, in their own particular ways. 

Developer Happy Hour with Cloudera: Building Hadoop 2 Applications

Join us at Cloudera’s San Francisco office on Feb. 20 for tech talks, T-shirts, and adult refreshments!

As an extension of the DeveloperWeek Conf & Festival 2014 experience in San Francisco next month, join us at Cloudera’s San Francisco office for a Developer Happy Hour (beer + tech talks), focusing on Apache Hadoop 2 application development. Anyone (attendees or non) is free to attend, but RSVP now because seats (and “Data is the New Bacon” T-shirts) are limited!

The Hadoop FAQ for Oracle DBAs

Oracle DBAs, get answers to many of your most common questions about getting started with Hadoop.

As a former Oracle DBA, I get a lot of questions (most welcome!) from current DBAs in the Oracle ecosystem who are interested in Apache Hadoop. Here are few of the more frequently asked questions, along with my most common replies.

Top 10 Blog Posts of 2013

From Python, to ZooKeeper, to Impala, to Parquet, blog readers in 2013 were interested in a variety of topics.

Clouderans and guest authors from across the ecosystem (LinkedIn, Netflix, Concurrent, Etsy, Stripe, Databricks, Oracle, Tableau, Alteryx, Talend, Twitter, Dell, Concurrent, SFDC, Endgame, MicroStrategy, Hazy Research, Wibidata, StackIQ, ZoomData, Damballa, Mu Sigma) published prolifically on the Cloudera Developer blog in 2013, with more than 250 new posts — basically, averaging one per business day.

This Month in the Ecosystem (November 2013)

Welcome to our fifth edition of “This Month in the Ecosystem,” a digest of highlights from November 2013 (never intended to be comprehensive; for completeness, see the excellent Hadoop Weekly).

With the holidays upon us, the news in November was sparse. Even so, the ecosystem never stops churning!

Managing Multiple Resources in Hadoop 2 with YARN

An overview of some of Cloudera’s contributions to YARN that help support management of multiple resources, from multi resource scheduling in the Fair Schedule to node-level enforcement

As Apache Hadoop become ubiquitous, it is becoming more common for users to run diverse sets of workloads on Hadoop, and these jobs are more likely to have different resource profiles. For example, a MapReduce distcp job or Cloudera Impala query that does a simple scan on a large table may be heavily disk-bound and require little memory. Or, an Apache Spark (incubating) job executing an iterative machine-learning algorithm with complex updates may wish to store the entire dataset in memory and use spurts of CPU to perform complex computation on it.

Things For Which We Are Thankful

Some things for which we are thankful, the 2013 edition (not listed in order):

1. The entire Apache Hadoop community for its constant and hard work to Make the Platform Better,

Approaches to Backup and Disaster Recovery in HBase

Get an overview of the available mechanisms for backing up data stored in Apache HBase, and how to restore that data in the event of various data recovery/failover scenarios

With increased adoption and integration of HBase into critical business systems, many enterprises need to protect this important business asset by building out robust backup and disaster recovery (BDR) strategies for their HBase clusters. As daunting as it may sound to quickly and easily backup and restore potentially petabytes of data, HBase and the Apache Hadoop ecosystem provide many built-in mechanisms to accomplish just that.

Putting Spark to Use: Fast In-Memory Computing for Your Big Data Applications

Our thanks to Databricks, the company behind Apache Spark (incubating), for providing the guest post below. Cloudera and Databricks recently announced that Cloudera will distribute and support Spark in CDH. Look for more posts describing Spark internals and Spark + CDH use cases in the near future.

BinaryPig: Scalable Static Binary Analysis Over Hadoop

Our thanks to Telvis Calhoun, Zach Hanif, and Jason Trost of Endgame for the guest post below about their BinaryPig application for large-scale malware analysis on Apache Hadoop. Endgame uses data science to bring clarity to the digital domain, allowing its federal and commercial partners to sense, discover, and act in real time.

This Month in the Ecosystem (October 2013)

Welcome to our fourth edition of “This Month in the Ecosystem,” a digest of highlights from October 2013 (never intended to be comprehensive; for completeness, see Hadoop Weekly).

For generating sheer excitement, that month installed a high bar to meet in the future:

Migrating to MapReduce 2 on YARN (For Operators)

Cloudera Manager lets you add a YARN service in the same way you would add any other Cloudera Manager-managed service.

In Apache Hadoop 2, YARN and MapReduce 2 (MR2) are long-needed upgrades for scheduling, resource management, and execution in Hadoop. At their core, the improvements separate cluster resource management capabilities from MapReduce-specific logic. They enable Hadoop to share resources dynamically between MapReduce and other parallel processing frameworks, such as Cloudera Impala; allow more sensible and finer-grained resource configuration for better cluster utilization; and permit Hadoop to scale to accommodate more and larger jobs.

Migrating to MapReduce 2 on YARN (For Users)

In Apache Hadoop 2, YARN and MapReduce 2 (MR2) are long-needed upgrades for scheduling, resource management, and execution in Hadoop. At their core, the improvements separate cluster resource management capabilities from MapReduce-specific logic. They enable Hadoop to share resources dynamically between MapReduce and other parallel processing frameworks, such as Cloudera Impala; allow more sensible and finer-grained resource configuration for better cluster utilization; and permit Hadoop to scale to accommodate more and larger jobs.

In this post, users of CDH (Cloudera’s distribution of Hadoop and related projects) who program MapReduce jobs will get a guide to the architectural and user-facing differences between MapReduce 1 (MR1) and MR2. (MR2 is the default processing framework in CDH 5, although MR1 will continue to be supported.) Operators/administrators can read a similar post designed for them here.

Terminology and Architecture

Cascading, Spring, and Spark: Development Choices for CDH Users Expand

In software development, there is no substitute for having choices. Furthermore, freedom of choice – between frameworks, APIs, and languages — is a major fuel source for platform adoption across any successful ecosystem.

In the case of development on CDH, the open source core of Cloudera’s Big Data platform containing Apache Hadoop and related ecosystem projects, the choices have expanded dramatically in the past three weeks:

Tips for Debugging Distributed Systems

Among Cloudera’s engineer-presenters at Strata + Hadoop World 2013 this week, Philip Zeyliger (“Tricks for Distributed System Debugging and Diagnosis“) was particularly fortunate to have been interviewed by O’Reilly Media editor Meghan Blanchette on camera.

In the following 8-minute interview, Philip offers an overview of common pain points and failures when debugging distributed systems:

See You Next Week at Strata + Hadoop World 2013!

Strata

We are just a weekend away from the Biggest. Strata + Hadoop World. Ever.

Guide to Special Users in the Hadoop Environment

There are a number of special “users” with roles to play in the Apache Hadoop environment. For your reference, we have summarized them below as of CDH 4.4. Kerberos principals (used for authentication in a secure cluster) are not covered here.

The specific user IDs listed are the ones created by default on installation but they are configurable unless otherwise indicated.

Apache Hadoop 2 is Here and Will Transform the Ecosystem

The release of Apache Hadoop 2, as announced today by the Apache Software Foundation, is an exciting one for the entire Hadoop ecosystem.

Cloudera engineers have been working hard for many months with the rest of the vast Hadoop community to ensure that Hadoop 2 is the best it can possibly be, for the users of Cloudera’s platform as well as all Hadoop users generally. Hadoop 2 contains many major advances, including (but not limited to):

Writing Hadoop Programs That Work Across Releases

In a fast-moving project like Apache Hadoop, there are always exciting new features introduced in each release. While it is tempting to make the most of these new features by upgrading to the latest release, users are often concerned about their code continuing to run.

In this post, you’ll get an overview of the the Hadoop API annotations and compatibility policies. Hadoop annotates specific APIs to be safe for use by end-users. By using these APIs, users can ensure their code works across a set of releases and be aware of what releases it might not work against.

Hadoop Releases and Compatibility

Customer Spotlight: Learn How Edo Closes the Advertising Loop with Hadoop at Cloudera Sessions Milwaukee

The Cloudera Sessions fall series is coming to a close next week, but first we’ll make a final stop in Milwaukee, Wisconsin (on Oct. 17), where attendees will hear about edo — a company that is revolutionizing the advertising space by closing the loop between promotions and point-of-sale transactions.

In Milwaukee, edo CTO Jeff Sippel will engage in a fireside chat with Cloudera’s VP of marketing, Alan Saldich. At edo, Jeff is responsible for the strategy, planning, and execution for the systems — including Apache Hadoop — that power the edo offer platforms.

Where to Find Cloudera Tech Talks Through December 2013

Below please find our regularly scheduled quarterly update about where to find tech talks by Cloudera employees this year – this time, for October through December 2013. Note that this list will be continually curated during the period; complete logistical information may not be available yet.

As always, we’re standing by to assist your meetup by providing speakers, sponsorships, and schwag!

Date City Venue Speaker(s)
Oct. 1 Aarhus, Denmark GOTO Aarhus Eva Andreeason on Hadoop use cases
Oct. 8 Sunnyvale, Calif. Hadoop Happy Hour Kathleen Ting and Jarek Cecho sign books!
Oct. 9 Santa Clara, Calif. IEEE BigData Conference Amr Awadallah on Hadoop use cases
Oct. 9 San Francisco SF Hadoop Users Eric Sammer on Hadoop app development (panelist)
Oct. 10 Sydney DataCon Sean Owen on data science
Oct. 15 Durham, NC TriHUG Mark Miller on Solr+Hadoop
Oct. 15 Mountain View, Calif. Oracle NoSQL & Big Data Meetup Mike Olson on virtues of key-value stores
Oct. 15-17 Burlingame, Calif. Big Data TechCon Apache Hive workshop with Mark Grover
Doug Cutting on the Hadoop revolution
Hadoop app development (CDK) workshop with Ryan Blue
Jonathan Seidman on extending data infrastructure with Hadoop
Jonathan Seidman on the Hadoop ecosystem
Himanshu Vashishtha on HBase use cases
Kate Ting on Apache ZooKeeper
Kate Ting on 7 Deadly Hadoop Misconfigurations
Oct. 16 Dallas, Tex. DFW Big Data John Ringhofer on Impala
Oct. 17 Milwaukee, Wis. Cloudera Sessions Hadoop app development lab (on CDK) with Ryan Blue
Oct. 17 St. Louis, Mo. St. Louis HUG Tom Wheeler on Parquet
Oct. 18 Munich HUG Munich Lars George on Impala
Oct. 22 London UK HUG Sean Owen on Scalable Big learning
Oct. 23 Seattle Seattle Scalability Meetup Ronan Stokes on Cloudera Search
Oct. 24 Palo Alto, Calif. Bay Area HBase User Group Michael Stack on HBase 0.96
Oct. 24 Raleigh, NC All Things Open Josh Wills on open source innovation
Oct. 28-30 New York Strata Conference + Hadoop World 2013 Mike Olson on Hadoop’s impact on data management
Doug Cutting on the future of Hadoop
Henry Robinson on workload diversity in Hadoop
Hadoop app development (CDK) workshop with Eric Sammer
Matt Brandwein on leveraging mainframe data with Hadoop
Aaron T. Myers and Shreepadma Venugopalan on Hadoop security
Jayant Shekar on machine data analytics
Amandeep Khurana on Monsanto’s use case for Hadoop & HBase
Philip Zeyliger on debugging distributed systems
Greg Rahn on Impala performance tuning
Jon Hsieh on HBase roadmap
Oct. 28 New York NYC HUG Arvind Prabhakar on Apache Sentry (incubating)
Oct. 28 New York Sqoop User Meetup Abe Elmahrek on the Sqoop2 app for Hue
Oct. 29 New York Impala + Parquet Meetup Greg Rahn on Impala+Parquet performance tuning
Oct. 29 New York Cloudera Manager Meetup Aditya Achara on Cloudera Manager success stories
Oct. 30 New York Apache Sentry User Meetup Arvind Prabhakar and Shreepadma Venugopalan with a Sentry overview
Oct. 30 Philadelphia Chariot Data IO Conference Lars George on HBase sizing as well as on Parquet
Nov. 6 Chantilly, Va. Open Source Search Conference Alex Moundalexis on Search+Hadoop
Nov. 6 Munich JAX Munich Lars George on HBase and Impala
Nov. 7 Tokyo Cloudera World Tokyo Kiyoshi Mizumaru on CDH
Sho Shimauchi on Cloudera Manager
Tatsuo Kawasaki witha Hadoop 101
Daisuke Kobayashi on Hadoop ops
Nov. 11 London UK HUG Marcel Kornacker on Impala
Nov. 12-13 London Strata London Sean Owen on Scalable Big Learning; Tom White on Hadoop app development with CDK
Nov. 12 San Francisco QCon SF Josh Wills on machine learning
Nov. 13 Washington DC LISA 2013 John Ridley on Hadoop 101 for sysadmins
Nov. 14 Seoul Tech Planet Korea Michael Stack on HBase roadmap
Nov. 14 Tokyo Cloudera Manager Meetup Sho Shimauchi, Kiyoshi Mizumaru: What is Cloudera Manager?
Nov. 14 Antwerp Devoxx Belgium Tom White on building Hadoop apps with CDK
Nov. 16 Los Angeles Big Data Camp LA Alex Behm on Impala
Nov. 20 Boulder, Colo. Boulder/Denver Big Data Meetup John Darrah on Hadoop 101
Dec. 2 Tokyo Cloudera Manager Meetup Sho Shimauchi, Kiyoshi Mizumaru: What is Cloudera Manager?

Let a Thousand Hadoop How-Tos Bloom

History teaches us that ecosystem growth is fueled by enthusiasm, tools (including frameworks and APIs), and knowledge in roughly equal measures. To this point, the Apache Hadoop ecosystem has been blessed with the first two ingredients – thanks to the magic of open source – but in the third category, there is still plenty of work to be done.

This Month in the Ecosystem (September 2013)

Welcome to our third edition of “This Month in the Ecosystem,” a digest of highlights from September 2013 (never intended to be comprehensive; for completeness, see Hadoop Weekly).

Note: there were a few other interesting developments this week, but out of respect for the calendar, I’ll address them next month.

Customer Spotlight: Persado Makes Marketing a Data Science

It’s common to hear people describe themselves as being “left-brained” or “right-brained” based on their tendency to be more logical and mathematically driven (left-brained), or, conversely, to be intuitive and creatively driven (right-brained). For example, people who prefer math over art are often considered left-brained. People who get a higher verbal score on their SATs than for math are often considered right-brained.

In general, language and creative writing are considered right-brained exercises. Many people also associate marketing and advertising as a right-brained function, whereas engineering is considered very left-brained.

Meet the Project Founder: Josh Wills

In this installment of “Meet the Project Founder,” we speak with Josh Wills (@josh_wills), Cloudera’s Senior Director of Data Science and founder of Apache Crunch and Cloudera ML.

What led you to your project idea(s)?
When I first started at Cloudera in 2011, I had a fairly vague job description, no real responsibilities, and wasn’t all that familiar with the Apache Hadoop stack, so I started working on various pet projects in order to learn more about the tools and the use cases in domains like healthcare and energy.

Hadoop Administrator Training Gets Hands-On

I’ve always held a strong bias that education is most effective when the student learns by doing. As a developer of technical curricula, my goal is to have training participants engage with real and relevant problems as much as possible through hands-on exercises. The high rate at which Apache Hadoop is changing, both as a technology and as an ecosystem, makes developing Cloudera training courses not only demanding but also seriously fun and rewarding.

I recently undertook the challenge of upgrading the Cloudera Administrator Training for Apache Hadoop. I more than quadrupled the amount of hands-on exercises from the previous version, adding a full day to the course. At four days, it’s now the most thorough training for Hadoop administrators and truly the best way to start building expertise.

Secrets of Cloudera Support: Impala and Search Make the Customer Experience Even Better

In December 2012, we described how an internal application built on CDH called Cloudera Support Interface (CSI), which drastically improves Cloudera’s ability to optimally support our customers, is a unique and instructive use case for Apache Hadoop. In this post, we’ll follow up by describing two new differentiating CSI capabilities that have made Cloudera Support yet more responsive for customers:

Email Indexing Using Cloudera Search

Why would any company be interested in searching through its vast trove of email? A better question is: Why wouldn’t everybody be interested? 

Email has become the most widespread method of communication we have, so there is much value to be extracted by making all emails searchable and readily available for further analysis. Some common use cases that involve email analysis are fraud detection, customer sentiment and churn, lawsuit prevention, and that’s just the tip of the iceberg. Each and every company can extract tremendous value based on its own business needs. 

Next Stops for The Cloudera Sessions: Jersey City, Miami, Denver, Milwaukee

Cloudera Sessions

In its first leg of its tour of the United States earlier this year (see photos here), The Cloudera Sessions proved to be an invaluable single-day event for business and technical leaders exploring practical applications of Apache Hadoop. So valuable, in fact, that we’ve extended the tour with dates/cities this September and October.

This Month in the Ecosystem (August 2013)

Welcome to our second edition of “This Month in the Ecosystem.” (See the inaugural edition here.) Although August was not as busy as July, there are some very notable highlights to report.

How-to: Select the Right Hardware for Your New Hadoop Cluster

One of the first questions Cloudera customers raise when getting started with Apache Hadoop is how to select appropriate hardware for their new Hadoop clusters.

Although Hadoop is designed to run on industry-standard hardware, recommending an ideal cluster configuration is not as easy as delivering a list of hardware specifications. Selecting hardware that provides the best balance of performance and economy for a given workload requires testing and validation. (For example, users with IO-intensive workloads will invest in more spindles per core.)

Hadoop 2 is Now a Beta

As announced last Sunday (Aug. 25) on the project mailing list, Apache Hadoop 2.1.0 is the first beta release for Hadoop 2. (See the Release Notes for full list of new features and fixes.) Our congratulations to the Hadoop community for reaching this important milestone in the ongoing adoption of the core Hadoop platform!

With the release of this new beta, and the follow-on GA release on the horizon, we expect to see more customers exploring Hadoop 2 for production use cases. In fact, the upcoming CDH5 beta will be based on the Hadoop 2 GA release, delivering features that we’ve thoroughly tested against enterprise requirements, including (but not limited to):

Visualization on Impala: Big, Real-Time, and Raw

The guest post below is provided by Justin Langseth, Founder & CEO of Zoomdata, Inc. Thanks, Justin!

What if you could affordably manage billions of rows of raw Big Data and let typical business people analyze it at the speed of thought in beautiful, interactive visuals? What if you could do all the above without worrying about structuring that data in a data warehouse schema, moving it, and pre-defining reports and dashboards? With the approach I’ll describe below, you can.

How Improved Short-Circuit Local Reads Bring Better Performance and Security to Hadoop

One of the key principles behind Apache Hadoop is the idea that moving computation is cheaper than moving data — we prefer to move the computation to the data whenever possible, rather than the other way around. Because of this, the Hadoop Distributed File System (HDFS) typically handles many “local reads” reads where the reader is on the same node as the data:

Spotlight: How National Institutes of Health Advances Genomic Research with Big Data

This week, I’d like to shine a spotlight on innovative work the National Institutes of Health (NIH) is working on, leveraging Big Data, in the area of genomic research. Understanding DNA structure and functions is a very data-intensive, complex, and expensive undertaking. Apache Hadoop is making it more affordable and feasible to process, store, and analyze this data, and the NIH is embracing the technology for this reason. In fact, it has initiated a Big Data center of excellence — which it calls Big Data to Knowledge (BD2K) — to accelerate innovations in bioinformatics using Big Data, which will ultimately help us better understand and control various diseases and disorders.

Bob Gourley — a friend of Cloudera’s who wears many hats including publisher of CTOvision.com, CTO of Crucial Point LLC, and GigaOm analyst — recently interviewed Dr. Mark Guyer, the deputy director of the NIH’s National Human Genome Research Institute (NHGRI), about the BD2K effort.

Flexpod Select with Cloudera

Earlier this week, our partners NetApp and Cisco announced the Flexpod Select Family with support for Cloudera’s Distribution including Apache Hadoop (CDH).

We’re looking forward to the expansion of Flexpod Select to include Hadoop, as it provides additional options for customers to consume the benefits of the Cloudera Enterprise platform.

Cloudera at Strata + Hadoop World 2013

Strata Conference + Hadoop World 2013 is looming on the horizon and pacing to be the largest gathering of Big Data professionals on the globe. As co-hosts with O’Reilly, we have seen the conference thrive, grow, and are excited about the upcoming Oct. 28 – 30 event!

Strata

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