ECO FALL 2022

This session is for Oracle Professionals, including analysts, DBA's, and developers looking to leverage their skillsets and retool into the well paying, red hot, and exciting field of Data Engineering.

Data Engineers build systems to convert raw data into usable information.  They use techniques they call data platforms, big data pipelines, and data science workflows.

In this session we explore how to translate our hard earned Oracle skillsets into this new paradigm. Beyond just the buzzwords, you'll learn the top 8 skills you'll need and give you a path of study to achieve them.  We will explore the data lake-house strategy from an Oracle and Open Source perspective, and how to build skills in each of these areas:
1 Data Lake
2 Data Acquisition
3 Data Enrichment
4 Data Reshaping and Modeling
5 Data Distillation
6 Machine Learning
7 Data Presentation
8 Data Visualization

This session will take you through 8 short steps you can take today to tap into the red-hot field of data engineering.  These include how to leverage your Oracle skills to cloud strategies, unstructured data, Apache Spark, Spark SQL, Python, R, and Scala.  We explore the most marketable ways to solve data at scale, and how to transition your hard earned Oracle skills into this new area.  There will also be an invitation to an 8 part hands-on series of online training sessions to further build data engineering skills.

Production patching and upgrades expose organizations to significant risks. Lower environments don’t reliably predict post-upgrade behavior if their topologies, infrastructure, and configuration aren’t identical to production targets or when synthetic load profiles don’t accurately mimic their real-world counterparts. What if there was a way to test your next upgrade in production safely and repeatedly using actual production loads? To compare the before and after performance of identical systems, side-by-side, in AWR reports? To practice and perfect the upgrade process, catching and fixing performance issues before go-live? These capabilities are available and easier to achieve than you might think. Learn how to bulletproof your next upgrade, replacing hope and luck with Data Guard Snapshot Standby and Real Application Testing!

This session will discuss using Data Guard to protect a standby database, prepare it for an upgrade, and leverage Real Application Testing to generate and replay production workloads. We’ll also cover how to capture database activity, preserve performance metrics for comparison, and revert the standby database to its original state.

This session will discuss technical enhancements to autonomous databases, cloud wars between the leading vendors, what you must learn to survive, and understanding which technologies you need to learn to adapt.

Life is good until the phone rings, a performance issue. Most DBAs will immediately run an AWR or Statspack report. Usually, that’s a good start. However, most DBAs will either stare at the report and freeze, start paging through looking for something or post the report on a forum. Neither of these solutions is a career-enhancing maneuver.

This session will cover how to take an AWR report or a Statspack Report, extract only the information you need, build a diagnostic framework to expose the biggest tuning opportunities, and then develop solutions that directly address them. This step-by-step process can be used on any Oracle version, architecture, configuration, cloud, or on-premise. It’s fast, it works, and it rocks! Join me as we explore how to quickly find solutions to real performance problems.

Many challenges and hurdles had to be overcome since the databases served a global financial client base. Production database migration to the Exadata environment had to be performed with zero downtime architecture with a “live cutover” while continuing to process application data. Exadata clusters consisted of two sets of ten node RAC compute nodes across two data centers.

Technologies leveraged include Datapump, GoldenGate, Real Application Testing, 10 Node RAC Cluster, Global Data Services, and 10 Node RAC Cluster Exadata replicating data to another 10 Node RAC Cluster Exadata.