10012026 - TechTalk Recap - Oracle Resiliency—Choosing the Right Data Protection Approach🛡️
If you missed our latest TechTalks session, Lester Wells and I explored an important reality for Oracle teams: there is no single data-protection feature that covers every failure scenario. Keeping Oracle resilient means looking beyond a simple “backup or HA” decision. The right approach layers protection for local failures, site failures, operational recovery, and logical corruption—then aligns each layer to the application’s business requirements. What you missed 👇 🧭 HA and DR are different problems. High availability helps applications continue through infrastructure failures; disaster recovery protects against a broader site-level event. Both matter, but not every database needs the same RPO or RTO. ⚡ Snapshots improve operational recovery. Fast, space-efficient snapshots can help reduce backup and recovery pressure, refresh dev/test environments, support reporting copies, and provide recovery points for ransomware or user errors. 🏢 ActiveCluster is designed for continuous availability. For metro-distance deployments that meet latency requirements, synchronous replication can provide zero RPO and zero RTO protection against array or site failure. 🌍 ActiveDR extends protection across distance. Continuous, asynchronous replication supports disaster recovery where synchronous replication is not practical—and enables non-disruptive DR testing. 🧩 Layered protection covers more failure modes. Oracle RAC, ActiveCluster, ActiveDR, Data Guard, Flashback, and snapshots each address different risks. Combining the right layers helps protect both availability and recoverability. 🔎 Replication does not replace logical recovery. A bad write, accidental delete, or corruption can replicate correctly. Point-in-time snapshots and database-level recovery tools remain important parts of the plan. One of the best practical examples was using ActiveDR to bring up and validate a DR environment without disrupting production. That makes it easier to test your recovery plan more often—before you need it. 🎥 Catch up on the session HERE🎥 💬 Which Oracle resilience challenge is top of mind for your team: backup windows, recovery time, site-level availability, DR testing, or protection from logical errors? Share your perspective in the comments.AI Can Write Your Data Platform. Who’s Going to Keep It Running?
AI can write SQL, generate pipelines, build infrastructure, create database migrations, and even diagnose production problems. But what happens when it confidently does the wrong thing? As AI becomes part of how we build and operate modern data platforms, the key question is no longer just, “Can AI generate this?” It’s whether we can trust the result, deploy it safely, observe what happens, and recover when things go wrong. Join Hamish Watson for a practical session on bringing AI-assisted development into production reality. Drawing on experience across SQL Server, cloud platforms, Databricks, and database DevOps, Hamish will explore how version control, CI/CD, automated testing, drift detection, observability, and governance help keep AI-generated changes safe and recoverable. The session will include practical examples, lessons learned, and a live demo. And, because live demos rarely go exactly to plan, there may even be a surprise or two. AI might be able to write your data platform. The more important question is: who’s going to keep it running? Register for the session
Oracle Resiliency: Choosing the Right Data Protection Approach for Every Failure
October 1 | Register Now Oracle recovery requires more than a backup strategy. Accidental deletion, logical corruption, infrastructure failure, site loss, and ransomware demand different combinations of backup, snapshots, and replication. This session explains where each data protection layer does and doesn’t belong in an Oracle recovery architecture. Through practical failure scenarios, we’ll examine how to align RPO, RTO, retention, availability, and recovery-location requirements with the right recovery mechanism. You’ll also learn how to identify overlap, avoid common recovery gaps, and use storage automation to apply consistent protection policies across production, development, test, and DR environments. Session takeaways: Map Oracle failure scenarios to the appropriate data recovery mechanism. Identify protection overlap, operational complexity, and recovery gaps before an incident exposes them. Translate workload requirements into practical RPO, RTO, retention, availability, and recovery-location decisions. Register Now!The Vocabulary Problem Hiding Inside Your AI Strategy
Every enterprise I talk to right now is having some version of the same conversation: what do we upgrade, what do we expand, and how do we get ready for agents that need to touch everything. What's interesting is where that conversation actually stalls. It's rarely budget, and it's rarely the roadmap. It stalls because the room doesn't agree on what the words mean. "Data primacy." "Ontology." "Data lineage." "Enterprise data cloud." I hear these used almost interchangeably in vendor decks and internal strategy docs, and that's the problem: they're not synonyms. They're four different layers of the same stack, each answering a different question, and each one shows up as its own line item in your next upgrade cycle. Get the distinction wrong and you'll buy the wrong thing, staff the wrong team, or promise your board something your architecture can't actually deliver. Here's how they fit together, and why the difference matters for every expansion decision between here and wherever your AI transformation is headed. The 50-year assumption AI just broke For as long as most of us have built enterprise IT, applications came first and data came second. Each app owned its own dataset, held its own context, enforced its own definition of things. "Customer" in your CRM meant something different than "customer" in your billing system. That was fine, because a human was always in the loop to reconcile the difference. AI agents don't have that luxury. An agent reasoning across sales, support, and finance at the same time can't pause and wait for a human to translate between five definitions of "customer." It needs broad, timely, trustworthy access to data that means the same thing everywhere it shows up. That's the fracture point, and it's exactly why data primacy, ontology, and data lineage have all become urgent at once. They're the three things you need to fix that fracture, each doing a different part of the job. Data primacy: the "why" and the architecture Data primacy is the strategic answer to the app-centric problem: flip the model so data is primary and applications become secondary, interchangeable consumers of it. Everpure's CEO Charlie Giancarlo frames this around three principles, and honestly, they double as a decent audit checklist for your own architecture. Semantics travel with the data, not locked inside any one application. Data is conserved, not copied: one authoritative, governed source serves every agent and app that touches it. And governance lives at the data layer itself, not bolted on through app-level access controls. This is the executive-level framing. When you're building the case for why the next three years of infrastructure spend should look nothing like the last ten, data primacy is the language that resonates in that room. It's a statement about how IT should be architected, not a technical spec. Ontology: the "how" that makes it real Data primacy's first principle, that semantics travel with the data, doesn't happen on its own. Something has to formally define what a piece of data is, what it means, and how it relates to everything else in the enterprise. That something is an ontology: the properties, attributes, and classification schema that make raw data self-describing, instead of dependent on whichever application happens to be reading it. If data primacy is the destination, ontology is one of the roads that gets you there. It's a data-layer, semantic-layer capability: the dictionary and grammar that make your data readable by anything, including an agent that's never seen your schema before. Everpure Data Intelligence (formerly 1touch) is built specifically for this layer, using AI-driven classification to create a business-aware ontology that evolves as your data does, rather than going stale the moment your data model changes. Worth a look if ontology is anywhere on your upgrade list. Data lineage: the historical record that earns trust Ontology tells you what data means. Lineage tells you where it's been: the audit trail from origin, through every transformation, to wherever it's being used today. This is the layer that turns into a legal and regulatory requirement faster than the other two. When a regulator asks why your AI model rejected a loan applicant, "lineage" is the only acceptable answer. The EU AI Act, GDPR, and NIST frameworks are all raising the documentation bar here, and lineage is quickly moving from nice-to-have to table stakes: teams need to know where data originated, how it was transformed, and precisely which models it fed. Enterprise data cloud: where all three actually get operationalized Data primacy, ontology, and lineage are principles and capabilities. Somewhere, they have to live on real infrastructure: on-prem, edge, and public cloud, without turning into three separate management problems. That's what an enterprise data cloud is for. Think of it as the layer that turns data primacy from a philosophy into something you can actually run: a unified, virtualized layer of data governed by an intelligent control plane, delivered as-a-service, with automation and resilience built in rather than stitched together after the fact. It's the difference between managing storage and managing data: every array, every location, every cloud, under one system instead of a dozen silos each running their own rules. This is also where "delivered as a service" and "intelligent control plane" stop being slide-deck language and start showing up in practice: rebalancing and mobility, compliance monitoring and remediation, agentic workflows via MCP, all running on a unified data plane,(Everpure's enterprise data cloud) instead of bolted onto individual boxes. Evergreen//One is the consumption model that makes this deliverable as a service rather than a capital project. How the four concepts actually fit together Read top to bottom, and it's a stack, not a list. Data primacy sets the destination. Ontology and lineage are the two roads that get you there, one carrying meaning, the other carrying history. The enterprise data cloud is the ground all three roads are paved on. Why this vocabulary problem is actually a strategy problem Here's the part that matters for your next planning cycle. If your team talks "ontology" when the business actually needs a data primacy conversation, you'll end up buying a classification tool when leadership wants a mandate to change how every application relates to data. Talk "data primacy" to a room of data architects who need lineage documentation for an upcoming audit, and you'll sound like a slide deck instead of a plan. Every upgrade, every expansion, every step deeper into agentic AI is going to force this same translation problem. The stack doesn't care what you call it, but your budget, your compliance posture, and your ability to safely let agents loose on your data all depend on knowing which layer you're actually solving for. Get the vocabulary straight, and the roadmap gets a lot easier to write. Want to go deeper on evaluating your own ontology? Watch my conversation with Argenis Fernandez, Nike's Distinguished Software Engineer, on why enterprises still need one. Register hereWhy Enterprises Still Need an Ontology
Data Under Pressure User Group Join us for a session with Argenis Fernandez, Distinguished Engineer at Nike, on Why Enterprises Still Need an Ontology. 📅 September 23, 2026 | 9:00 AM PST 🔗 Register: https://purestorage.zoom.us/webinar/register/WN_sK9Bkfz6QrmXxXKzLBzTCw#/registration What you'll get: Why talking the same language across your org matters A walk through the four-layer enterprise data stack A 4-cell rubric for evaluating your own ontology layer Don't miss this one if you're thinking about data architecture, governance, or how to get teams speaking a common language.
The Art of Sizing: How to Size an Exadata Migration Without Overbuying Storage
If you are moving Oracle databases off Exadata, the first sizing mistake is usually looking at only one database at a time. Exadata is not a traditional Oracle architecture. Some of the work that would normally happen on the database host is performed by the storage cells, including capabilities such as Storage Indexes, Smart Scan, and—depending on the database version and workload—vector offloading. That changes what the storage numbers mean. A successful migration therefore needs to answer two different questions: How much I/O is the current Exadata environment really performing? Which parts of that work will move to the database hosts, change shape, or disappear when the architecture changes? This installment of The Art of Sizing walks through a practical way to answer both questions. Why Exadata sizing is different In a conventional Oracle environment, it is common to begin with the database-level system statistics and identify the read and write workload for that database. That is still useful on Exadata, but it is not enough. Exadata is often a consolidated platform running multiple databases across multiple storage cells. The migration target must be sized for the estate as a whole, not just for whichever database happens to be easiest to measure. The other difference is architectural: Exadata reports include physical work performed by the cells. That work can include processing that a non-Exadata design may need to perform elsewhere. The implication is simple: Do not copy an Exadata number directly into a replacement-storage calculator and assume the result is equivalent. First, understand what the number includes. Then normalize it for the target architecture. Start with the familiar metrics—but use the global view For a normal Oracle workload, the starting point is the System Statistics (Global) view. The key measurements are: Read IOPS: physical read total IO requests Write IOPS: physical write total IO requests Read MB/s: physical read total bytes Write MB/s: physical write total bytes These measures establish the workload profile, but a per-database view can hide the scale of a consolidated Exadata environment. Figure 1: The Global Activity Statistics view provides the starting point for reading Oracle read and write activity. The detailed statistics show the specific physical-read and physical-byte measures that should be carried into the analysis. Figure 2: Example physical-read measures from the global statistics output. The important word here is global. If Exadata is hosting several databases, collect the workload across the full environment. A replacement platform must be able to absorb the combined demand, including concurrent peaks that may not be visible when databases are reviewed independently. Use the Exadata AWR Global RAC report to see the platform as a whole The Exadata AWR Global RAC report is valuable because it brings the consolidated environment into view. It shows the disk types, cell counts, disk counts, IOPS, and bandwidth associated with the Exadata storage layer. In the example from the source report, the cells are performing approximately: 615,244 IOPS 28,899 MB/s Those values come from the F/6.2T and H/20.0T disk groups shown in the report: 404,115 IOPS plus 211,129 IOPS equals approximately 615,244 IOPS 24,297 MB/s plus 4,602 MB/s equals approximately 28,899 MB/s Figure 3: Example Exadata storage-cell performance summary. These are useful sizing inputs, but they are not automatically the final requirements for the migration target. They describe the physical work being performed inside the Exadata architecture. That distinction matters because Exadata is doing more than simply storing blocks. Account for the work Exadata is doing for you When you leave Exadata, some of the platform behavior may need to be reproduced through database design, host resources, or application changes. The exact impact depends on the workload, database version, SQL mix, and target architecture, so the assessment must be validated with the DBA and application teams. Exadata capability or behavior Migration sizing question Storage Indexes Will equivalent filtering be provided by indexes, partitioning, clustering, or SQL changes? Smart Scan Will more data be returned to the host because the target platform does not perform the same cell-side filtering? Vector offloading Where will vector-related processing occur, and what CPU, memory, and I/O will it require? Consolidated storage cells Will the target platform handle the same concurrency and peak overlap across all databases? Large SGA requirements Does the target need additional host memory to preserve cache behavior and avoid turning cell-side efficiency losses into repeated reads? Table and index layout Do large tables need new indexes, partitioning, or access-path changes to deliver comparable performance? This is the part of the migration that is easiest to underestimate. The goal is not merely to reproduce the observed Exadata I/O. The goal is to understand the work behind that I/O and decide where it will run after the migration. Normalize ASM redundancy before sizing capacity The Exadata summary also shows how much storage is allocated and used through ASM. This is where capacity can be overstated if the redundancy model is carried into the new architecture without question. The source example uses ASM High Redundancy. In ASM, that means the database data is stored with three copies. If the target storage platform supplies its own protection and the design uses ASM External Redundancy, the database layer may need only one logical copy. Figure 4: Example ASM disk groups with High Redundancy and their allocated and used capacity. As a first-order capacity normalization, moving from three ASM copies to one logical copy reduces the ASM-replicated requirement by approximately 66.7 percent. For example, 600 TB represented under a three-copy ASM model could correspond to roughly 200 TB of logical database data before adding target-platform protection, snapshots, growth, recovery copies, and operational headroom. That is not a universal storage-sizing answer. It is a normalization step. Before using it in a purchase or design, confirm: Whether External Redundancy is appropriate for the target architecture How the storage platform protects data and handles failure domains Whether the design includes snapshots, replication, backup staging, or disaster recovery copies How much free space and growth headroom the environment requires Whether the capacity numbers are raw, usable, allocated, or actually consumed The key lesson is that database redundancy and storage redundancy should be evaluated together. Counting both without understanding the division of responsibility can lead to buying protection twice. Do not reduce performance by the same percentage automatically It is tempting to apply the same 66.7 percent reduction to IOPS and MB/s because the ASM model moves from three copies to one. That can be a useful initial hypothesis, but it is not a guaranteed performance result. Read and write behavior can change differently. Exadata cell processing may be removed. Host-side indexes or partitioning may add work. The target storage platform may also use a different protection scheme, data-reduction method, caching model, or concurrency profile. A better approach is to use the Exadata numbers as a measured baseline, then build a target model with explicit assumptions: Baseline physical read and write IOPS Baseline read and write bandwidth Peak and average behavior across the full database estate Expected change in host-side processing Expected impact of indexes, partitioning, and larger SGA requirements Target protection and redundancy model Growth, recovery, and availability requirements That model gives the migration team something it can test instead of a single inflated number or an overly optimistic discount. A practical Exadata migration sizing workflow The process can be summarized in six steps: Collect the Global RAC and Exadata AWR reports for the full environment. Record average and peak IOPS, bandwidth, storage utilization, disk types, and database concurrency. Separate database workload from Exadata cell-side processing and identify what may move to the hosts. Normalize ASM redundancy so database copies are not counted twice with storage protection. Add target-specific requirements for CPU, memory, indexes, partitioning, snapshots, replication, recovery, and growth. Validate the model with short-window performance data and a representative migration or proof of concept. The first four steps create the sizing baseline. The last two turn that baseline into a design that can survive real workloads. The sizing lesson Exadata migration sizing is not a matter of copying the largest number from an AWR report into a storage proposal. It is an exercise in separating three things: The work the current environment is performing The protection and redundancy that are being counted today The work and protection the target architecture will need tomorrow In the example discussed here, the Exadata cells show roughly 615,244 IOPS and 28,899 MB/s, while the ASM configuration indicates High Redundancy with three copies of the data. Those are important facts, but they are starting points—not automatic target requirements. The most reliable design is the one that measures the whole estate, understands the Exadata features behind the measurements, removes duplicated redundancy, and then adds back the host, protection, recovery, and growth requirements of the destination platform. That is the art of sizing an Exadata migration: not buying for the biggest number, but understanding what the number actually represents. Final thought When an Exadata migration is undersized, the problem usually appears after the architecture is already difficult to change. When it is oversized, the organization pays for capacity and performance that may never be used. The better path is disciplined normalization. Measure Exadata as a consolidated system. Identify which work will move. Separate database redundancy from storage protection. Then size the target for the workload you are actually building—not the architecture you are leaving behind. That is how sizing turns a migration estimate into an engineering decision.Actionable Assessments: Prepare Your Infrastructure for Oracle 26ai
August 20 | Register Now As organizations adopt Oracle AI Database 26ai with capabilities like AI vector search, understanding whether their existing data infrastructure can support these new workloads becomes increasingly important, while keeping costs under control. Join us as we explore how to analyze peak workload concurrency, optimize storage redundancy, and map advanced feature dependencies. Attendees will walk away with an actionable framework for evaluating their own environment, including access to a complimentary, privacy-first Everpure readiness assessment to help automate the planning process. Key takeaways: See your true workload and capture a consolidated, hour-by-hour view of peak demand. Crush storage bloat and learn how real-world environments are safely shrinking their storage footprints. Learn how to align infrastructure for active features like TDE, HCC, and Smart Scan without over-buying hardware. Secure your assessment and get exact sizing recommendations. Register Now!The Art of Sizing: The Seven Signals That Help Decide Oracle 26ai Readiness
The Art of Sizing — Series Categories: Databases · Oracle · AI and Machine Learning By Thomas Stutesman, Principal Field Solutions Architect, Everpure A migration readiness scorecard turns raw Oracle AWR telemetry into seven plain-language signals — and shows you exactly where a lift-and-shift would carry yesterday's problems into tomorrow's platform. The migration everyone is planning for Across the industry, many organizations are now looking to move to Oracle AI Database 26ai. The release promises autonomous efficiency, AI-driven insight, and native vector search — but for most teams the path forward is clouded by one uncomfortable question: is our current infrastructure sized for tomorrow, or are we carrying the weight of yesterday's inefficiencies? The 26ai Migration Readiness scorecard is one of the helpful tools Everpure has created to make that migration easier. It takes the telemetry you already have — your Oracle Automatic Workload Repository (AWR) reports — and translates it into a clear, consistent read on where each database stands, so you move with evidence instead of assumptions. This is the first idea in our Art of Sizing series: for a decade, cheap flash and fast cores let us over-provision and "throw hardware at it." Oracle 26ai — with real vector workloads and a hardware market where components cost more and take longer to arrive — ends that era. Disciplined sizing is back, and it starts with reading the right signals. What the scorecard shows Point the assessment at an estate and it produces a single view: every database, scored across seven signals, rolled up into a combined readiness score. In one illustrative example across eight databases, the combined readiness score was 118 out of 168 — with 0 Ready, 7 Caution, and 1 Risk. None were a clean lift-and-shift: seven needed validation and tuning, and one needed remediation before it could move. Each database is scored across the seven signals with a simple traffic light, and the lights roll up into a score out of 21 — seven signals, three points each: Ready — 3 points — inside the healthy threshold Caution — 2 points — worth validating Risk — 1 point — attend to before you move A score of 18–21 is favorable, 13–17 means review needed, and below 13 flags remediation. Think of it as a current-state indicator that tells you where the risk concentrates — not a migration certification. A red flag doesn't mean "cannot migrate." It means the problem will follow you into 26ai — or get worse — if you size the new environment from the old box instead of from the evidence. The seven signals, one by one These are the value items to worry about when sizing for and moving to 26ai. For each, here is what it reads from AWR, why it drives your target design, what a red is telling you, and how Everpure helps you act on it. Signal 01 — System Capacity Reads DB Time against available CPU cores, average active sessions, and host CPU pressure. This sets the compute floor for the target and drives per-core Oracle licensing — size to the old ceiling and you inherit the old bottleneck. On top of that, 26ai's vector and embedding work adds fresh CPU demand. Red means: the source is already CPU-bound; moving as-is relocates the constraint. You need real headroom, not a like-for-like footprint. How Everpure helps: the assessment reports peak (not averaged) CPU demand per database and translates it into a right-sized core count — so the target is provisioned for the real workload plus deliberate headroom, and licensing is planned rather than guessed. Signal 02 — SQL & Parse Reads hard-parse rate, literal (non-bind) SQL, library-cache and cursor-sharing behaviour. 26ai changes optimizer behaviour, so heavy hard-parsing burns CPU, pressures the shared pool, and invites plan regressions on the new release. Red means: application-level SQL that will keep burning CPU or regress on cutover. Plan-stability work (SQL Plan Management, baselines) and shared-pool sizing belong before the move. How Everpure helps: because the assessment looks at the whole estate, not one instance, it surfaces shared SQL/parse patterns across databases — so one remediation effort (often "add bind variables") protects many migrations and lowers the CPU you have to size and license for. Signal 03 — Wait Profile Shows how DB Time splits across CPU, I/O, concurrency, and commit/log waits. It tells you what kind of bottleneck you are actually sizing for — a commit-bound database and an I/O-bound one need very different target designs. Red means: DB Time is dominated by a problematic wait (log file sync, buffer busy, latch/enqueue). Sizing CPU or storage without addressing it just moves the pain. How Everpure helps: Everpure maps the dominant wait class to the right lever — low, predictable write latency on FlashArray for commit/log waits; CPU or memory changes for concurrency waits — so the target attacks the real bottleneck instead of over-buying everywhere. Signal 04 — I/O Profile Reads read/write IOPS, throughput, block size, and latency. This is the direct input to storage sizing, and it separates latency-sensitive OLTP from bandwidth-driven scan/DW workloads — which size very differently. Averages hide the peak windows that actually test the array. Red means: real I/O demand the target tier must sustain. Under-provision here and everything above it — waits, capacity, response time — degrades. How Everpure helps: the assessment moves beyond averages to hour-by-hour peak-window analysis and turns it into a concrete IOPS/throughput/latency target for FlashArray — sub-millisecond and consistent, so the design performs at peak, not just on paper. Signal 05 — Memory Reads SGA/PGA sizing, buffer-cache behaviour, and Oracle's own memory advisories. Right-sizing memory on the target avoids trading RAM savings for a flood of avoidable physical I/O — and under-sized memory quietly inflates the I/O profile, so the two signals move together. Red means: memory is under-provisioned and driving physical reads that should be cache hits. Size SGA/PGA to demand rather than copying the old config. How Everpure helps: Everpure correlates memory pressure with observed I/O, so recommendations account for both together — enough memory to cut needless physical reads, and storage sized for what genuinely reaches disk. Signal 06 — Temp & Work Reads temp tablespace usage, sort/hash spills, PGA work-area activity, and multipass operations. Analytics, reporting, and AI-adjacent workloads live or die on temp and PGA — and 26ai vector operations add sort/compute patterns you have not sized for before. Red means: large sorts/hashes are spilling to temp; without PGA and temp sizing, the workload is slow on day one regardless of platform. How Everpure helps: the assessment quantifies spill behaviour and feeds it into PGA and temp-tier sizing, while FlashArray's consistent low latency keeps unavoidable spill cheap rather than catastrophic. Signal 07 — Segment Risk Reads large or fast-growing segments, chaining, LOBs, partitioning, and use of features like TDE, Hybrid Columnar Compression, and Smart Scan. These are the objects that don't migrate cleanly — reorg candidates, feature-compatibility items, and capacity-growth landmines. Red means: schema-level cleanup, not just capacity: objects that need reorg, or that depend on features the target must be configured to support from day one. How Everpure helps: Everpure auto-detects active features (TDE, HCC, Smart Scan) so the target is configured for them without over-provisioning, and replaces full clones and legacy triple-copy redundancy with space-efficient snapshots and external redundancy to reclaim capacity. From evidence to a right-sized migration The scorecard is the front door. Behind it is an assessment built to turn these seven signals into decisions you can defend to procurement and operations alike. It captures every instance for an ecosystem view, replaces multi-day averages with peak-window analysis, and translates raw metrics into specific storage and configuration recommendations for a 26ai target. It runs privacy-first — hostnames and SQL identifiers are masked, and only the actionable results are shared. Sometimes the biggest finding is what you don't need to buy. One prospect expected to buy 200 TB. The workload needed 18 TB. The gap was legacy ASM high-redundancy (three data copies) plus full clones for dev/test. Moving to Oracle-recommended external redundancy on Everpure and space-efficient snapshots reclaimed roughly two-thirds of the footprint and eliminated over 180 TB of wasted capacity — exactly the kind of trap that nameplate-based sizing would have locked in. From there, a right-sized design lands on a platform built for database density: Everpure FlashArray//XL R5 delivers sub-millisecond, consistent latency and industry-leading data reduction that absorbs vector growth economically. Pure Fusion presets encode your sizing discipline once so every 26ai environment is provisioned the same way, and Pure1 adds fleet-wide performance, capacity forecasting, and application-level context. A sizing-first path to 26ai Assess. Send your AWR reports; the 26ai Readiness Assessment is generated automatically, with identifiers masked. Interpret the signals. Reds are the pre-migration punch list; cautions are the validate list. Watch for fleet-wide patterns. Remediate reds first. Fix CPU, SQL/parse, dominant waits, and segment issues before cutover. Right-size the target. Size compute, memory, temp/PGA, and storage to the measured peak — and strip out redundancy and clone inflation. Provision consistently. Use Pure Fusion presets so every environment is built to the same standard. Validate with a pilot. Confirm behaviour against a representative pilot and target telemetry before scaling the wave. Summary Oracle 26ai brings back the art of sizing. The 26ai Migration Readiness scorecard reads seven signals from your AWR data — System Capacity, SQL & Parse, Wait Profile, I/O Profile, Memory, Temp & Work, and Segment Risk — so you can remediate the reds, right-size the target to your real workload, and move to 26ai with confidence instead of guesswork. Know where you stand — send us an AWR report, and Everpure's Oracle experts will run the 26ai Readiness Assessment on your environment and walk you through the seven signals, your remediation punch list, and a right-sized target design. The readiness score is an AWR-derived, current-state indicator to guide investigation and planning. It is not a migration certification, a final target-platform sizing, or a target-array headroom calculation — those decisions require workload requirements, target telemetry, compatibility checks, and a representative pilot. Database figures shown are illustrative. Oracle is a trademark of Oracle Corporation. © 2026 Everpure, Inc.
Navigating the Tsunami, Reshaping the Data Industry
August 18 | Register Now! For August 2026, host Andrew Miller invites Andy Yun to Coffee Break. As a DBA himself with a long career in the data + database field, Andy now talks to Everpure customers (or sometimes “future customers”) every day about data and database challenges and solutions. We’ll wander through: Andy’s history with key mentoring lessons (both those he has learned and has helped others with), the value of soft skills in a deeply technical role, and why a solution architect works at Everpure. Database & DBA landscape - simultaneously keeping up “business as usual” while adapting to the AI tsunami that’s here whether we like it or not. Andy’s top DBA & database tips on Everpure from chatting with DBAs and customers every day. What’s new in the data landscape at Everpure - everything from an Oracle 26ai readiness assessment, to mind-blowing SQL backup throughput numbers, to how recent Everpure acquisitions impact you. Register Now!
