By Ashwin Kota, HEXstream integrations and analytics lead \nIn the world of databases, especially with Oracle, there are several features designed to keep data safe and operations smooth. One critical feature is Oracle Archive Logs. Understanding what archive logs are, how they work, and the benefits and challenges they bring can help us see just how valuable they are for data management.\nWhat are Archive Logs?\nArchive logs are essentially copies of Oracle’s redo log files. Redo logs capture every change made to the database, helping Oracle keep track of transactions. When these redo logs fill up, Oracle “switches” to a new redo log file, and the filled log file is then “archived.” Archiving these logs means storing them in a safe location on the disk rather than overwriting them.\nIn simple terms, archive logs act like a historical record of all database transactions. They help create a trail of changes, which is invaluable for data recovery, point-in-time restoration, and much more.\nThe Lifecycle of Redo and Archive Logs\nHere’s a quick look at the stages of redo and archive log management:\nRedo Log Generation: As transactions are processed, changes are recorded in redo logs.\nLog Switching: When the redo log file fills up, Oracle switches to a new one.\nArchiving: The filled redo log file is then archived, creating an archive log.\nLog Cleanup: After successful backups, archive logs can be deleted to free up storage space.\nWhy are Archive Logs Used?\nData Recovery: Archive logs are crucial for recovering lost data. If there’s a system failure or accidental deletion, archive logs can restore the database to a previous state by applying the changes from the archived logs.\nPoint-in-Time Recovery: Archive logs allow for precise recovery to a specific moment. If an error occurs, the database can be restored to just before that time. This is valuable for handling unintended changes or even malicious actions.\nContinuous Backup: Oracle archive logs support backups while the database is live, allowing for continuous operation without downtime. A full backup combined with archived logs means you can restore data to any point after the backup.\nData Replication: Archive logs can replicate data to another database, creating a standby database for disaster recovery or business continuity.\nAdvantages of Archive Logs\nEnhanced Data Security: Archive logs improve data security by enabling a complete history of changes. In case of errors, the database can be restored with minimal data loss.\nFlexible Recovery Options: Archive logs support point-in-time recovery, letting administrators choose the exact state to which the database is restored. For example, if a ransomware attack occurs, archive logs can restore the database to its state before the attack, protecting data integrity.\nReduced Downtime: Because archive logs support live backups, businesses with high availability needs (like banks and e-commerce platforms) can avoid long downtimes.\nImproved Disaster Recovery: By leveraging archive logs, businesses can replicate data to another database at a different location. This ensures that, even in the event of a major disaster, data can still be retrieved.\nDisadvantages of Archive Logs\nHigh Storage Needs: Archive logs take up significant space, which can affect storage costs and capacity. Regular cleanup and storage management are necessary to prevent archive logs from overwhelming server space.\nPerformance Overhead: Managing archive logs requires additional processing power, which can slightly harm overall database performance.\nManagement Complexity: Setting up archive logs can be complex, requiring careful configuration and monitoring. Ensuring proper storage allocation, monitoring, and cleanup processes adds complexity.\nTips for Managing Archive Logs Effectively\nMonitor Storage Usage: Regularly check the space used by archive logs. Use the following command to viewyour archive log destination:\nsql\nCopy code\nSHOW PARAMETER LOG_ARCHIVE_DEST;\nAutomate Backups: Backing up archive logs regularly is essential for both data recovery and space management. You can automate backups using RMAN (Recovery Manager):\nsql\nCopy code\nRMAN> BACKUP ARCHIVELOG ALL;\nAutomate Log Deletion: To prevent archive logs from filling up storage, configure automatic deletion of old logs. The following command deletes archive logs older than seven days:\nsql\nCopy code\nDELETE ARCHIVELOG ALL COMPLETED BEFORE
'SYSDATE-7';\nAlways ensure you have a recent backup before deleting old logs.\nUse a Retention Policy: Set a retention policy for archive logs to keep only the logs needed for the most recent recovery point. This approach helps save space and simplifies log management.\nEnable Archiving Carefully: Enabling archiving requires a few simple steps, but it is essential to do it correctly to avoid errors:\nCheck the Archive Log Mode:\nsql\nCopy code\nARCHIVE LOG LIST;\nShut Down and Mount the Database:\nsql\nCopy code\nSHUTDOWN IMMEDIATE;\nSTARTUP MOUNT;\nEnable Archive Logging:\nsql\nCopy code\nALTER DATABASE ARCHIVELOG;\nOpen the Database:\nsql\nCopy code\nALTER DATABASE OPEN;\nHow to Use Archive Logs for Replication\nIn environments that require high availability, archive logs can be used to replicate the database to a secondary location. This replication helps in setting up a “standby” database, which is a copy of the primary database kept up-to-date by continuously applying changes from archive logs. This setup provides redundancy and allows the secondary database to take over if the primary database fails.\nPractical Scenarios for Archive Logs\nRecovering from Accidental Deletion: If a user accidentally deletes data, archive logs enable recovery to just before the deletion.\nProtection Against Ransomware: If ransomware affects a database, archive logs can be used to roll back to a time before the attack, preserving data integrity.\nDisaster Preparedness: In areas prone to natural disasters, archive logs facilitate quick replication to an off-site database, ensuring data is safe even in emergencies.\nConclusion\nOracle archive logs are a powerful tool in the world of data management. They are essential for ensuring data integrity, supporting flexible recovery options, and maintaining efficient backup processes. With proper configuration and management, archive logs provide enhanced security, reduced downtime, and robust disaster-recovery options, making them invaluable for any critical database environment.\nIn today’s data-driven world, effective use of Oracle archive logs can transform your approach to data management. By enabling archive logging and following best practices for monitoring, backing up, and cleaning up logs, businesses can achieve a highly reliable, secure and resilient database system.\n\nLEARN HOW ORACLE ARCHIVE LOGS CAN BE USED AT YOUR UTILITY. CLICK HERE TO CONNECT WITH US. \n


The Power Of Oracle Archive Logs: Essential For Data Recovery And Beyond

By Satish Saini, HEXstream utilities industry specialist  \nRecent Hurricanes Helene and Milton have highlighted how extreme weather events are affecting America’s power transmission and distribution infrastructure. (In a nutshell, they are exposing serious cracks.)   \nThe efficient management of fleet and field-crew services and resources are critical for quick power restoration, to minimize outages and to ensure safety for citizens. But efficient management isn’t always in place. Smart utilities are leveraging advanced technologies through coordinated and collaborated efforts, enabling them to effectively launch large-scale recovery operations based on improved decision-making insights. \nLet’s explore some of the key technologies being used to manage fleet and field-crew services efficiently: \nGeographic Information Systems (GIS) \nprovide spatial visualization and mapping capabilities, helping identify and prioritize areas impacted by hurricanes with meaningful information on affected grid assets, customer pockets and track-crew locations in real time with updated estimated times of restoration (ETRs). \nMobile workforce & fleet-management systems \nhelp\n \nstreamline field operations by providing dispatchers and field crews with real-time updates, job assignments, and work-order management during emergencies. Equipped with GPS and telematics, these systems include capabilities like mobile workorders, automated job-scheduling, real-time communication, and route-optimization for fleets to access assignments, to report progress, and to update the status of restoration tasks. \nAdvanced Distribution Management Systems (ADMS) including Outage Management (OMS) \nplatforms integrate various utility operations with detailed information on grid assets and infrastructure, including real-time monitoring of the distribution network, fault location and isolation, and some automated switching operations. These systems help utilities direct field crews to the most critical areas, reducing outage duration by detecting which feeders, circuit sections and assets are out of service and suggesting the best course of action to restore power, which guides field crews accordingly. \nAdvanced Metering Infrastructure (AMI)\n data through relevant analytics plays a key role in enhancing outage detection, response and restoration efforts for utilities. By leveraging real-time data from customer smart meters along with their exact locations, utilities can improve operational efficiency, efficiently managing field crews and resources to reduce downtime and enhance customer satisfaction. \nDrone and aerial-inspection technology \nequipped with high-resolution cameras and thermal sensors have the capability to inspect power lines and infrastructure for damage in hard-to-reach or hazardous areas, reducing the need for manual inspections. These tools quicken damage assessment. \nData analytics & data-management systems \nthrough data-storage\n \nplatforms enable utilities to store, access, share and analyze critical data related to network assets, outage history, past events and operational data and crew assignments. Cloud infrastructure ensures data availability and continuity even during power outages, which are key elements in disaster recovery. In particular, cloud-based storage systems equipped with efficient data-synchronization provide a centralized hub for data-sharing and collaboration, ensuring that all crews, whether on-site or remote, are working with consistent and up-to-date information. \nSome of the leading technology solutions and service providers for utilities include ADMS and OMS by various vendors like ABB, GE, Shneider and Survalent. Oracle Field Service Cloud offers end-to-end field-service management with real-time scheduling, routing, and mobile capabilities. IBM Maximo is a comprehensive asset and workforce-management solution that supports field operations and integrates with other enterprise systems.  \nAnd HEXstream’s Utility360 combined with our AMI Analytics offering is an efficient solution to effectively manage field crews, outages ETRs and reliability indices reporting. \nData management and analytics play a pivotal role with the massive volumes of data generated by multiple sources at modern utilities; this data is related to operations, outages, asset-management, field crews and workforce management, and customer-information systems. \nHEXstream, with our decades of expertise in all of these fields, can guide utilities to not just weather the current turbulent state of utilities, but absolutely thrive within it.  \n\nWANT YOUR FIELD CREWS TO WORK SMARTER? CONNECT WITH US HERE TO LEARN HOW HEXSTREAM CAN HELP.\n

Work Is Getting Harder For Fleets And Field Crews, Their Tools And Technologies Are Getting Smarter

By Ashwin Kota, HEXstream integrations and analytics lead \nElectricity is something we often take for granted. When we flip a switch, we expect the lights to turn on and the TV to hum to life. However, maintaining this consistent flow of power is no easy task. Utilities work hard to keep power reliable and avoid interrutions. \nBut how do they measure how well they’re doing?\nIn this article we’ll take a look at four key metrics that utilities use to measure the reliability of electric systems: SAIDI, SAIFI, CAIDI and MAIFI. Let’s see what these metrics mean and why they matter to all of us.\nWhy Track Reliability Metrics?\nReliability in the electric system is all about the dependability of the power supply. Tracking reliability metrics helps utilities figure out where they need to make improvements. The goal is to keep power interruptions—also known as outages—as rare and short as possible. By studying reliability, utilities can see where they are doing well and where they need to make changes. This improves customer satisfaction, as fewer people experience the inconvenience of outages.\nUnderstanding the Key Reliability Metrics\nWhen it comes to reliability, there are four main metrics utilities look at. These metrics, often created using outage data, help utilities understand both the frequency and the duration of interruptions.\nSAIDI (System Average Interruption Duration Index):\n SAIDI measures the average amount of time that customers experience outages in a given year. If the SAIDI score is 120 minutes,that means the average customer experiences a total of two hours of outages each year. Lower SAIDI values mean fewer or shorter outages, which is a good thing.\nSAIFI (System Average Interruption Frequency Index):\n While SAIDI focuses on how long outages last, SAIFI looks at how often they happen. If the SAIFI score is 1.5, it means that, on average, customers experience about 1-2 outages per year. Like SAIDI, a lower SAIFI score is better, as it means fewer interruptions.\nCAIDI (Customer Average Interruption Duration Index):\n CAIDI tells us how quickly the utility can restore power when an outage does happen. To get CAIDI, divide SAIDI by SAIFI; f SAIDI is 120 minutes and SAIFI is 1.5, CAIDI would be 80, meaning the average time to restore power is around 80 minutes. Utilities aim to keep CAIDI as low as possible so customers don’t stay without power for long.\nMAIFI (Momentary Average Interruption Frequency Index):\n Unlike the other three metrics, MAIFI focuses on very brief interruptions (those lasting less than five minutes). These short interruptions are common but can still be annoying. MAIFI helps utilities track these momentary blips in service and figure out ways to reduce them.\nThe Role of Major Event Days (MEDs)\nNot all outages are created equal. Some are caused by routine issues, like equipment failures, while others happen due to large-scale events, like major storms, earthquakes, or wildfires. These large, unpredictable events are called Major Event Days (MEDs). Since these events can cause big disruptions, utilities separate them from the usual daily data to get a clearer picture of regular performance.\nLet’s break down how each metric is calculated with and without MEDs:\nSAIDI\nWith MEDs: When we include MEDs, SAIDI will be higher because these big events cause longer outages. This version shows the impact of rare but high-impact events on the system.\nWithout MEDs: By removing MEDs from SAIDI, we get an average that shows how the system performs on a normal day. This helps utilities understand the typical reliability of their system without the influence of extreme events.\nSAIFI\nWith MEDs: When calculated with MEDs, SAIFI shows how often outages happen, including during extreme events. This helps utilities plan for responses to emergencies.\nWithout MEDs: Removing MEDs from SAIFI shows how often interruptions occur in normal conditions. A lower SAIFI score here is a good sign of the system’s stability day-to-day.\nCAIDI\nWith MEDs: Including MEDs in CAIDI reveals how quickly power is restored after any type of outage, including major events. This gives an idea of how well a utility can respond during emergencies.\nWithout MEDs: Without MEDs, CAIDI shows how efficiently outages are resolved in regular situations. A lower CAIDI here suggests a utility is well-prepared to handle everyday interruptions swiftly.\nMAIFI\nWith MEDs: MEDs have less of an impact on MAIFI, as momentary outages are usually short and not caused by major events.\nWithout MEDs: Since MAIFI focuses on brief interruptions, it remains mostly stable regardless of MEDs. This score primarily helps track minor reliability issues that occur during normal operation.\nThe Difference Between With and Without MEDs is Important\nBy looking at metrics both with and without MEDs, utilities get a fuller picture of their grid’s reliability. Here’s why it matters:\nWith MEDs: Including MEDs in calculations shows the utility’s ability to handle severe disruptions. It helps the utility understand the grid’s resilience and prepare for worst-case scenarios.\nWithout MEDs: Calculations without MEDs provide a baseline, showing how well the system works under normal circumstances. This version of the metrics helps utilities improve routine reliability by targeting areas that need attention.\nTogether, these versions help utilities balance both daily reliability and readiness for rare events, enabling a more reliable and robust grid overall.\nHow Utilities Use Reliability Data\nThe information from these metrics doesn’t just sit on a shelf. Utilities use reliability data to make decisions about where and how to invest in the grid. Here are some examples:\nTargeting Problem Areas: By identifying the circuits with the worst SAIDI and SAIFI scores, utilities can focus their efforts on areas that need the most improvement. These “worst-performing circuits” often become the top priority for repairs and upgrades.\nPlanning for Future Needs: In regions where outages are more common, utilities can strengthen infrastructure or install new technology to prevent future interruptions. This could include adding automated systems that make it easier to detect and respond to outages quickly.\nTracking Improvement Over Time: By comparing metrics over the years, utilities can measure the effectiveness of their investments. A lower SAIDI or SAIFI over time shows that their reliability projects are working, which is a positive sign for both the utility and its customers.\nMoving Toward a Reliable Future\nReliability isn’t a one-time fix; it requires constant attention and improvement. By using these metrics, utilities can understand the strengths and weaknesses of their systems. This knowledge helps them make informed decisions that lead to a better, more dependable power supply. For customers, it means fewer power interruptions and quicker service restoration when outages do happen.\nIn short, SAIDI, SAIFI, CAIDI and MAIFI are the cornerstones of electric-system reliability. By keeping an eye on these metrics, utilities can keep power flowing smoothly and ensure that we rarely have to think about what it takes to keep the lights on.

Navigating The Modern Grid: A Deep Dive Into Utilities’ Reliability Metrics SAIDI, SAIFI, CAIDI & MAIFI

By Siddartha Reddy Katukuri, HEXstream integrations and analytics consultant \nFirst, let’s clarify what, exactly, we mean by “event streaming.”\nIn the utility industry, events refer to any significant data points or changes in state, such as
readings from meters, sensors, pipelines or transactional records. Event streaming enables applications and systems to act on data instantly, allowing
utilities to monitor consumption, predict outages, and optimize resource distribution
in real time.\nBy utilizing event streaming, businesses can unlock the full potential of their data. This
capability allows for real-time monitoring of consumption, predictive
maintenance, and optimized resource distribution, depending on the data at
hand. For example, energy providers can identify grid issues before they
escalate, improving preventive measures and service reliability. In industries
like water management, real-time sensor data can instantly detect leaks,
reducing resource wastage and enhancing service efficiency. \nEvent streaming ultimately enables companies to proactively respond to data-driven insights, fostering better business agility and service quality.\nHarnessing the Power of Oracle Integration Cloud and Apache Kafka for Event Streaming\nOracle Integration Cloud
(OIC) offers seamless integration capabilities with both cloud and on-premises
systems, supported by a wide range of pre-built adapters for various
applications. Apache Kafka’s architecture, optimized for high-scale and
high-performance environments, makes it ideal for organizations managing large
data volumes that require real-time insights.\nOIC’s toolkit extends to process automation, including workflow automation, approval routing, and task management. It simplifies data transformation with visual tools that map data
between systems, supporting conditional transformations. Kafka, in turn, brings
valuable features like event reprocessing and replay from specific points in
time, a critical function for fault-tolerant systems. This capability
complements OIC, which might otherwise need external storage for persistent
event handling.\nTogether, OIC and Kafka
create a powerful solution for modern, data-driven applications by combining
streamlined integration with resilient event streaming. This partnership
enables real-time data processing and efficient handling of large volumes,
enhancing agility and responsiveness across interconnected systems. For
example, in high-demand sectors like oil and gas, the OIC-Kafka combination
facilitates real-time monitoring of machinery and supply chains, enabling quick
responses to operational changes and ensuring continuous production. Businesses
seeking to optimize data flows and build scalable, reactive applications will
find OIC and Kafka to be a highly effective solution.\nEfficient Integration for Business Scenarios with OIC and Kafka\nOIC and Apache Kafka
provide a modern event-processing platform that meets diverse business needs
across industries. With OIC’s prebuilt adapters and Kafka’s effective
partitioning and topic management, this integration provides a flexible,
scalable solution for real-time data routing, workflow approvals, and business
flows. \nFor example, when Kafka
streams its data, OIC can trigger workflows to calculate energy consumption,
bill customers in real time via SMS or email, grid sensors disruptions and—if irregular
data points are detected—OIC workflows can initiate an alert for field teams to
investigate potential issues.\nApache Kafka’s
reprocessing and replay features make it an ideal choice for fault-tolerant
applications, maintaining data continuity during downtimes or recovery.
Although OIC does not inherently store messages, Kafka’s event retention allows
for long-term data storage and replay, adding resilience to event-driven architectures.
This pairing of OIC’s real-time integration strengths with Kafka’s fault
tolerance and data persistence is invaluable for modern applications.\nConnectivity between OIC and Kafka Streaming\nConnectivity between OIC
and Kafka is achieved through OIC’s prebuilt adapters, including the Apache
Kafka Adapter and Confluent connectors. These adapters enable direct
integration with Kafka, facilitating essential operations and enhancing
real-time data streaming and processing capabilities across platforms.\nThis seamless
connectivity is especially advantageous for companies requiring immediate data
availability across platforms such as Azure Event Hub. By leveraging Kafka’s
streaming capabilities alongside OIC’s integration, businesses in sectors like
healthcare can improve patient care through real-time data sharing across
applications. This direct connectivity ensures that systems can communicate
without time delays, helping organizations maintain data accuracy and operational
efficiency. As event data flows seamlessly, business ecosystems become more
responsive to changes, maintaining high performance across multi-cloud or
hybrid environments.\nTo ensure secure,
scalable integration connection, it’s essential to implement encryption,
authentication, and strong monitoring practices. Both OIC and Apache Kafka
provide advanced encryption capabilities including SASL, SSL/TLS, TDE and SFTP
encryptions to protect data throughout its lifecycle, from transfer to storage,
making them suitable for handling sensitive data across various industries,
including finance, utilities and healthcare.\nThe Future of Event Streaming with OIC and Kafka\nFuture advancements in
OIC and Kafka promise even tighter integration and more sophisticated event
handling, setting the stage for innovative applications across industries. \nFor sectors like utilities
and logistics, these advancements will mean greater accuracy in real-time
monitoring of infrastructure, tracking supply chains, and managing production cycles,
ensuring minimal delays and optimized resource allocation. \nAs the event streaming
landscape evolves, businesses that adopt and integrate these tools effectively
will be positioned to lead in their markets, harnessing real-time insights to
drive innovation and maintain a competitive advantage. Embracing the future of
event streaming with OIC and Kafka enables organizations to stay ahead of the
curve, adapting seamlessly to an ever-changing, data-driven world.\n


Integrating Oracle Integration Cloud (OIC) With Apache Kafka For Event Streaming

By Vaishnavi Medikundam, HEXstream BI developer\nOracle has added new facial-intelligence features to Oracle Cloud, offering innovative tools for businesses to enhance their data usage. In the rapidly evolving world of technology, Oracle Cloud is continually innovating, and this new feature is transforming how businesses use data.\nUnderstanding Facial Intelligence\nFacial intelligence goes beyond facial recognition. It's about decoding human expressions, emotions and interactions—providing a deeper understanding of user behavior. Oracle Cloud's new capabilities in this realm promise to change industries that rely on data-driven decision-making. \nKey Features\nOracle Cloud introduces precise facial recognition, emotion analysis, improved user authentication, and analytics/insights. \nOracle Cloud's upgraded facial-recognition system ensures unmatched accuracy, revolutionizing security in sectors like finance and healthcare. The system is equipped to analyze facial expressions, enabling businesses to gauge customer sentiments in real-time. This feature opens new avenues for personalized customer experiences and targeted marketing strategies. \nSay goodbye to traditional passwords. Oracle Cloud's facial intelligence can be integrated and provide a secure and convenient way for users to access sensitive information. \nThe gathered facial-intelligence data isn't just about recognition; it's about insights. Businesses can now leverage analytics to understand customer preferences, optimize workforce management, and make data-driven decisions. \nBenefits Across Industries\n·       Enhanced customer profiling fosters personalized shopping experiences, driving customer satisfaction and loyalty.\n·       Improved patient authentication and emotion analysis elevate healthcare outcomes and patient experiences.\n·      Strengthened security measures (including advanced fraud detection) that can fortify the financial sector against evolving threats. \nOracle Cloud's commitment to innovation continues to empower businesses worldwide. The introduction of advanced facial-intelligence capabilities heralds a new era in data-driven decision-making, promising efficiency, security and unparalleled insights. \nAs we embrace these new capabilities, the future looks brighter and more intelligent than ever before. 

Here's Looking At You: New Facial-Intelligence Capabilities On Oracle Cloud 

By \nGokuleshwar Raju Nannapuraju\n, HEXstream BI/ETL consultant\nAs you may have heard, Oracle has released version 2.9 of its Oracle Utilities Analytics Warehouse (OUA), bringing several new features and improvements. Let’s break down these updates in an easy-to-understand way for non-technical readers…\nSupport for Microservices Architecture for Configuring Oracle GoldenGate\nStarting with version 2.9, OUA now supports the Microservices Architecture for Oracle GoldenGate. This is a modern way to configure and manage Oracle GoldenGate, a tool that helps move data between different databases.\nSo what does this actually mean? \nSimpler and more efficient: Microservices make the configuration and management of data transfers more streamlined and scalable.\nOut with the old: The older "Classic" architecture for setting up Oracle GoldenGate is no longer supported. The configuration module "JKM BI Oracle to Oracle GoldenGate" has been updated to work with this new architecture.\nSupport for Oracle Analytics Server 2024\nOUA 2.9 is now compatible with Oracle Analytics Server version 2024 (version 7.6). It also continues to support the 2023 version. This is important, primarily, because of the enhanced analytics; with this support, users can leverage the latest features and improvements in Oracle Analytics Server 2024, providing better data analysis and visualization capabilities.\nUpdated Supported Platforms\nThe release includes updates on the platforms that are supported, ensuring compatibility with the latest server and client-side technologies.\nServer-side:\nOperating System (Server): Oracle Linux 7.x, 8.x (64-bit), Oracle Solaris 11.4 on
     SPARC (64-bit)\nFMW App Server: WebLogic 12.2.1.4.0\nJDK: JDK 8u411\nOracle Database: Enterprise Edition 19.x, Client for Linux 19\nOracle Analytics Server: 7.0 (2023) and 7.6 (2024)\nOracle Data Integrator (ODI): 12.2.1.4\nApex: 23.2\nOracle GoldenGate Microservices: 19.1\nOracle REST Data Service: 21.4.3\nClient-side:\nClient Operating System: Windows 10 (64-bit)\nBrowser Versions:\nMicrosoft Edge 120.0.2210.77\nMozilla Firefox 121.0\nGoogle Chrome 120.0.6099.109\nSupported Source Application Versions\nThe following source application versions are supported in this Oracle Utilities Analytics Warehouse release:\nCustomer Care and Billing: 2.9.0.1.1\nCustomer to Meter: 2.9.0.1.1\nNetwork Management System: 2.6.0.1.1, 2.3.0.2.0\nWork and Asset Management: 2.4.0.1.1\nMeter Data Management: 2.5.0.1.1\nMobile Workforce Management: 2.3.0.3.0, 2.2.0.x.x\nSupported Upgrade Paths\nThe following upgrade path is available for Oracle Utilities Analytics Warehouse in this release:\nFrom: V2.8.0.1.2 To: V2.9.0.0.0\nSummary\nExisting clients are encouraged to upgrade to OUA 2.9 because the new release supports all the latest versions of source applications and integrates seamlessly with new applications used in OUA. This upgrade will not only resolve existing bugs, but also introduce new features, providing a more robust and efficient analytics solution.\nAs for me, I'm excited to see these upgrades in action across utilities, as they will not only resolve existing issues, but also enhance compatibility with the latest OAS versions and new source applications. This will be incredibly beneficial for end users.\nClick \nhere\n for the official Oracle documentation. \nAnd click \nhere\n to connect with HEXstream to learn how we can help you take advantage of this upgrade. \n
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Oracle Updated OUA…So How Do You Benefit From It?

By Arun Kota, HEXstream senior director analytics\nAn intricately crafted disaster-recovery system stands as a vital pillar for utility organizations, ensuring swift and efficient recovery in the aftermath of catastrophes. In the dynamic landscape of IT, disasters take various forms, ranging from network outages and equipment failures to application glitches and natural calamities, which all pose imminent risks to the integrity of your applications. \nAs we can’t predict when a disaster will strike, the best option is to have a plan on how to quickly recover. \nAn important step in disaster-recovery planning is determining the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for an application. \nRecovery Time Objective (RTO) serves as a crucial metric, reflecting the maximum allowable downtime for a specific application following a disaster, with a general rule that emphasizes shorter RTOs for applications of higher criticality.\nRecovery Point Objective (RPO) signifies the timeframe, post-disaster, during which an application can endure data loss without negatively affecting the enterprise, underscoring the critical need for tailored recovery strategies based on data sensitivity and business-continuity priorities.\nIn crafting a robust disaster-recovery plan, it is imperative to factor in both RTO and RPO, built upon a strategic alignment that harmonizes the goal of timely application recovery with a tolerance for data loss. This dual consideration facilitates the development of a comprehensive, cost-effective, and resilient framework for disaster preparedness.\nMultiple Approaches for Disaster Recovery in OCI\nOracle Cloud Infrastructure (OCI) provides four different approaches in disaster recovery.
Selecting the appropriate disaster-recovery solution for an OCI application hinges on various factors such as availability needs, data durability, as well as the specific requirements for both RTO and RPO, necessitating a thoughtful evaluation to align the chosen solution with the organization’s unique considerations.\n1.    Backup and Restore–This approach backs up critical data and systems into offsite storage on regular intervals. In the event of a disaster, data can be restored from these backups to bring the systems back to their previous states. This is a relatively cheaper approach, but it usually takes more time. The backup-and-restore approach is usually used for noncritical applications.\n2.    Pilot Light–In this approach, a minimal version of the environment is always running in the cloud. In the event of a disaster, the infrastructure can quickly scale up to full capacity. The
RPO and RTO are shorter, but a little costly  compared to the backup-and-restore tactic. The
Pilot Light approach is usually used for critical applications.\n3.    Active/Passive or Warm Standby–In this approach, a fully installed and configured application is on standby and ready to run when required. This setup reduces the time required to switch to a fully operational state in the event of a disaster. The RPO is in seconds and RTO is in minutes; the cost is significantly more expensive than Pilot Light. The
Active/Passive or Warm Standby approach is usually used for critical applications.\n4.    Active/Active–In this approach, the applications are deployed in Active/Active mode and are actively serving. Every transaction is registered  in both applications. Both RPO and RTO are almost zero. This approach has the highest cost, as the applications are running in parallel. The Active/Active approach is used for extremely critical applications.\nOCI disaster recovery can be created using multiple regions. Choosing the right DR approach for an application is very important. The criticality of the applications, data consistency, cost and complexity need to be factored in when deciding the DR approach for an application.\n\nLEARN HOW HEXSTREAM CAN BOOST YOUR EFFICIENCY WITH ORACLE CLOUD INFRASTRUCTURE. CLICK HERE TO CONTACT US. \n\n


Recover Faster! New Approaches To Disaster Recovery With Oracle Cloud Infrastructure (OCI)

By Gokuleshwar Raju Nannapuraju, HEXstream integrations and analytics consultant\nIn this blog post, we navigate through the revolutionary impact of Oracle Cloud Infrastructure–Data Integration (OCI DI), offering a non-technical overview that illuminates its benefits and underscores why it stands as a true game-changer in reshaping the integration landscape for a seamlessly connected future.\nWhat Is OCI DI? \nOCI DI, an abbreviation for Oracle Cloud Infrastructure–Data Integration, serves as a cloud-based ETL (Extract, Transform, Load) tool designed to foster seamless connections between source and target databases. This powerful tool empowers users to efficiently cleanse, transform, and reshape data, streamlining the integration process for enhanced efficiency and reliability.\nUnderstanding Data Integration\nData integration involves the transformation of data from various sources, enabling seamless connectivity between different databases. This process includes connecting to source and target databases, cleansing and transforming the data through mapping and logic definition. Mapping from source to target databases enables data transformation either through manual execution or scheduled jobs based on desired frequencies.\nAdvantages of OCI DI\n1. Zero maintenance: Unlike traditional data-integration tools that require regular patching and service monitoring, OCI DI requires zero maintenance. Users can rest easy, knowing that they are free from any maintenance activities or concerns regarding application versions.\n2. No installation required: OCI DI eliminates the need for time-consuming installations. With a few simple steps, users can create a workspace and promptly start utilizing OCI DI.\n3. Basic configuration: Once the workspace is established, users can effortlessly connect to the source and target databases. OCI DI eliminates the need for complex configurations, ensuring a streamlined integration experience.\n4. Cost-effectiveness: OCI DI is an affordable option for businesses, and its unique pricing model charges users based on data usage and integration needs. There is no need to constantly start or stop services, as costs are calculated according to the actual usage.\n5. Enhanced speed: Regardless of the data volume being integrated, OCI DI utilizes powerful Spark engines in the background, ensuring high-speed execution once the job is initiated. This enables efficient data-integration processes for improved productivity.\nThe Significance Of Zero Maintenance\nLet’s circle back to that zero-maintenance benefit of OCI DI, which alleviates the burdens associated with managing services and ensuring timely startups. Users can enjoy uninterrupted integration capabilities without the need for downtime or patching activities. By eliminating these concerns, OCI DI optimizes business operations and maximizes efficiency.\nIn summary, OCI DI is transforming the integration landscape with its user-friendly features, cost-effectiveness, and maintenance-free experience. By leveraging the power of Oracle Cloud Infrastructure, businesses can enhance their data-integration capabilities and achieve seamless connectivity across various databases. Embrace OCI DI and unlock a new level of integration efficiency for your business.


Transforming Integration: The Evolving Benefits Of Oracle Cloud Infrastructure Data Integration

With the size and scope of collected data expanding with each passing day, it is very important to continually analyze available data to drive better-informed business and policy decisions. To a large extent, handwritten data remains unexplored and unanalyzed. If we can analyze handwritten text data, we can minimize the hurdles and save the manpower involved in digitizing handwritten data.\nIn this blog, we cover how handwritten text data can be processed using Python and Google Cloud Vision. Cloud vision offers pre-trained ML models which are very powerful, and we do not need to do any pre-training.\nBelow is an example of converting simple handwritten text into digital words, which can be easily ingested into a CSV file or database.\nUsing the applications listed in the instructions below, we are able to convert the scanned images or PDF files into digital text. Our approach for this solution requires us to first convert PDF documents to an image format. As we are processing images, it is better to convert all the images to the same size. We then need to define the source from which we are extracting the data from the image. This requires knowing the coordinates of the data to be extracted.\nSteps to Convert Handwritten Text into Digital Data\nThe prerequisites for this exercise are to install Google Cloud Vision, Python 3, Handprint, Keras, NumPy, pandas, pdf2image and cv2.\nConvert the PDF document to an image using the following python command.
\nimages = convert_from_path(file)\nSave the images to a directory so that this will be used for data extraction.
\nfor img in images:
   img.save(path + "\\" + fileName + '.jpg', 'JPEG')\nRead the image using following command.
\nimage = cv2.imread(filename)\n
\nPass the image to the pytesseract to convert the image to data item.
\nTo view the keys, run the below command.\nCheck the extracted text data.
\nExtract the coordinates for the required text using the following command.\nRepeat the same process for all the required text fields.\nUse the extracted coordinates to crop the image for the required field and save the cropped image to a specific location.
\nDownload the credentials file after setting up the Cloud Vision account on Google.
\nProcess these cropped images using Google’s Cloud Vision to extract handwritten values.
\nWe then extract the required data from the Json files.
\nWe have created a Python dictionary to store the extracted information. Convert the dictionary to dataframe for easy processing.
\nAs this example deals with only two images, the instructions did not include many validations. For a real-world application, analysts should validate the extracted data based on confidence levels and filter out the accurate data.\nIn Conclusion\nHandwritten image and text processing is very useful across many industries, including healthcare, insurance, and utilities. Various use cases include invoice processing, processing of inspection forms, employee onboarding, analyzing reviews and survey data and so on. One prescient application for utilities and other organizations relying on distributed field services is the digitization of handwritten notes taken by field technicians when completing repairs or regularly scheduled maintenance. By digitizing these notes, \nand even incorporating process automation to do so\n, utilities are better situated to retain critical handwritten information regarding asset health, maintenance history, and more.

In this blog, we cover how handwritten text data can be processed using Python and Google Cloud Vision. Cloud vision offers pre-trained ML models which are very powerful, and we do not need to do any pre-training.

How to Process Handwritten Text Using Python and Cloud Vision

Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.\nBefore understanding how to choose the best machine learning algorithms let us briefly look into the types of machine learning algorithms. The machine learning algorithm can be broadly classified into three groups.\nSupervised Learning Algorithm\nUnsupervised Learning Algorithm\nReinforcement Learning Algorithm\nRegression\nK-Mean Clustering\nMonte Carlo\nRandom Forest\nKNN\nMarkov Decision Process\nDecision Tree\nSVD\nQ Learning\nSupervised learning model: Trained with the dataset which consists of labels for both Input and output.\nUnsupervised learning model: Trained with the dataset that has input features but does not have labels for the output.\nReinforcement learning model: Trains itself based on a set of actions and makes the decision.\nNow, let’s learn when to use these algorithms rather than how to use these algorithms. Choosing the correct algorithm is dependent on multiple variables. A good understanding of data science plays a very significant role in categorizing the problem statement.\nThe factors we need to consider while categorizing and solving the problem are:\nKnowledge of Data\n: The data’s structure and complexity help dictate the right algorithm\nAccuracy Requirements\n: Different questions demand different degrees of accuracy, which influences algorithm selection\nProcessing Speed\n: Algorithm choice may depend on the time constraints in place for a given analysis\nVariables\n: The unique features considered while training the model for the optimal result and accuracy help determine the right algorithm.\nParameters\n: Factors such as the number of iterations directly relate to the training time needed when generating output.\nOur first step is to analyze the data and observe the patterns and any hidden insights by visualizing the data. The insights from data visualization will help in making an initial decision on which algorithm to choose for solving the given problem.\nAs an example, if the dataset has input and output labels, a supervised learning algorithm will be best for the problem. Supervised learning can be segregated further based on the type of output. For any numeric output, one should choose a regression algorithm to train the model, but for any class output, one should target a classification algorithm to train the model.\nIf the dataset has no output labels, choosing an unsupervised learning algorithm will be a wise decision, for example, the K-Mean clustering algorithm comes in handy in dividing the dataset into a smaller cluster or set based on similar attributes.\nThe characteristics and behavior of the dataset also play an important role in making the ideal algorithm selection. For example, if the data is sequential, like stock price or heart rate, then the algorithm that fits best is the Markov model and decision tree.\nAnother important factor in deciding the algorithm is speed and accuracy. There is a tradeoff between speed and accuracy. If accuracy is not vital, perhaps when estimating a value, we can reduce the processing time, thus increasing the execution speed. Conversely, we can achieve better accuracy by slowing the execution speed.\nAnother factor that plays an important role in choosing the correct algorithm is the number and types of parameters we pass while training the model. These parameters may include iteration cycle, splitting train and test datasets, error tolerance, and more. The training time for a given model is directly proportional to the number of parameters included. When multiple parameters are required to train the model, SVM (Support Vector Machine) algorithms work well.\nReal-Life Use Cases\nLinear Regression\nThis can be used to predict housing prices based on multiple variables including square footage, the presence of a balcony, the number of bathrooms, total rooms, kitchen area, etc.\nEach of these variables plays an important role in predicting the accurate housing price. A co-relation of 1 indicates a perfect relationship and accurate housing price, anything less than 0.9 can safely be assumed as unreliable prediction.\nLogistic Regression\nCurrently, bank customers face the constant threat of credit card and identity fraud. As a result, the financial sector is working to devise new techniques to avoid these problems. One of the steps banks have taken to reduce fraud is by leveraging logistic regression to build a classifier, known as the LogR classifier, which adapts to the user’s behavior based on his historic data. As a result, financial institutions can more accurately distinguish their true customers from any impersonator.\nSupport Vector Machine (SVM)\nThis helps in handwriting detection using a support vector machine classifier. It has achieved accuracy of up to 96% when \nconverting handwritten notes\n into text-based data.\nRandom Forest\nRandom Forest algorithms can be used to predict equipment failure and schedule just-in-time preventive maintenance. The failure of the equipment, such as utility poles or transformers, can be caused by cracks, corrosion pitting, spalls on rolling surfaces, and more. These failures can be catastrophic and financially devastate a given organization.\nTaking advance of Random Forest machine learning algorithms and AI to monitor these assets may help in \ndetecting failures at their earliest stages\n, reducing or preventing damage to the asset and the organization’s bottom line altogether.\nK-Mean\nUber, Lyft and other rideshare apps utilize K-Mean machine learning algorithms for optimal placing and clustering of their vehicles based on the peak demand. As a result, customers enjoy reduced wait times and helps the rideshare companies dictate surge pricing when supply is low and demand is high.\nIn Conclusion\nAs we have discussed, machine learning algorithms generally fall into one of three categories – supervised learning, unsupervised learning, and reinforcement learning. When addressing different types of business challenges, analysts should carefully consider the characteristics of the data, speed and accuracy requirements, variables, and parameters in order to yield the desired insights.\nBy using the proper algorithms, organizations can expect to benefit from stronger data insights that reflect the unique conditions that guide each business process. As their algorithms continually learn and improve, so will the data-driven decisions that result.

Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.

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