Latest news with #SREs
Techday NZ
6 days ago
- Business
- Techday NZ
Elastic launches Logs Essentials for cost-effective cloud log analytics
Elastic has announced the release of Logs Essentials, a new serverless log analytics tier offered within Elastic Cloud Serverless and designed for site reliability engineers (SREs) and developers. Logs Essentials is positioned as a lower-priced service to provide teams with essential log ingestion, searching, visualisation, and alerting without the requirement to manage the underlying infrastructure. The solution is built on the same stateless architecture as Elastic Observability, providing the ability to scale automatically and without operational overhead while retaining high availability. Core features The product enables users to perform fast and precise log analytics using filters, pattern matching, alerting, rich visualisations, and ES|QL, Elastic's piped query language. According to Elastic, this feature set is designed to help SREs quickly identify and resolve issues, improving the efficiency and effectiveness of response efforts to operational incidents. Santosh Krishnan, General Manager, Observability & Security at Elastic, commented: "SREs need a hassle-free, scale-as-you-go, high-availability logging solution that empowers them to focus entirely on operational insights, not infrastructure, without the complexity of standing up and maintaining observability tooling," Santosh Krishnan, general manager, Observability & Security at Elastic. "Logs Essentials makes it easy to get started with Elastic by offering a simple, reliable path to insights at a lower entry point." Logs Essentials is designed for teams that require core log analytics capabilities but are not seeking to pay for more advanced features. When more comprehensive observability is required, there is an upgrade path to Elastic Observability Complete, which includes further workflows and feature sets. Pricing and scalability Elastic has highlighted the tier's price-optimised model, where customers pay for the data they ingest and store, rather than committing to permanent infrastructure or premium licensing. This approach aims to make log analytics accessible for organisations of varying sizes, particularly those that want to avoid fixed costs or the complexities associated with on-premises deployments. The automatic scaling feature is managed through Elastic Cloud Serverless and is intended to maintain performance as log volume changes, especially during traffic spikes or incident investigations. The stateless design is noted as being central to enabling seamless scaling and system resilience. Operational insights Elastic states that Logs Essentials supports teams in accelerating root cause analysis and in obtaining deep contextual insights, as well as proactive detection of operational issues. The service is targeted to provide a "hassle-free entry point for operational insights," according to statements in the product description included in the release. Elastic also pointed to the popularity and existing adoption of its platform in the market, citing usage by thousands of companies, including more than half of the Fortune 500. Service availability Logs Essentials is now available within Elastic Cloud. Registration is managed via the provider's standard channels, and customers are able to begin with a free trial before choosing to purchase the service. The new tier joins Elastic's portfolio of solutions that integrate search, observability, and security applications, all built upon Elastic's Search AI Platform. Users can deploy the tier without infrastructure management responsibilities, and scale their deployment as needed according to log volume and analytic requirements.
India.com
05-05-2025
- Business
- India.com
Architect of Engagement: Bhaskar Yakkanti and the Personalized Data Era
Bhaskar Yakkanti In the bustling universe of hospitality, guest expectations evolve minute by minute. Bhaskar Yakkanti's Customer Data Platform (CDP)—deployed for a leading global hospitality-and-gaming group—answers that volatility with a lake-house architecture that fuses hotel bookings, gaming activity, event tickets, and on-property purchases into a single, privacy-hardened spine. Streaming collectors ingest five million events an hour, Spark enrichment jobs fuse behavioural traits with loyalty metadata, and the polished record lands in Azure Synapse views that marketers query in seconds. Revenue strategists can now retune campaign cohorts midway through a holiday weekend rather than waiting for next-day batches, a shift that clips acquisition spend by double-digit margins while lifting repeat-stay rates. The platform's backbone emerged from Bhaskar's conviction that lineage must travel with every row. A Python validation framework performs schema drift checks before data reaches downstream marts; failed records detour to a quarantine route, preserving analytic integrity without slowing the happy path. Even compliance auditors—once tethered to protracted lineage hunts—trace a guest's profile from kiosk swipe to marketing blast in fewer than ten clicks. The immediacy has reshaped decision cycles: revenue teams hold 'lunch-and-launch' huddles that swap static weekly reports for real-time dashboards, and property managers experiment with room-upgrade incentives that react to occupancy swell in under thirty minutes. 'My experience of implementing large-scale data pipelines is that clarity in customer intent dictates every design choice, and once the 'why' is visible, the 'how' aligns naturally,' Bhaskar notes, underscoring that his architecture privileges business context over tooling hype. Powering Instant Decisions in Financial Services Long before casino floors and resort towers, Bhaskar refined his craft inside a global payments institution struggling with fraud-screening latency. Legacy MapR clusters digested card-swipe logs overnight, leaving investigators to chase stale anomalies. Bhaskar replaced the batch monolith with Spark Structured Streaming, partitioned on issuer geography and card family, then emitted micro-batches every three seconds to a rules-engine sandbox. Incident-response windows compressed from hours to minutes; false-positive escalations dropped by 22 percent; and the institution reclaimed seven figures in dispute fees during the first quarter post-cutover. Equally transformative was his decision to lay observability rails before performance tuning. He instrumented Kafka topic lag, executor memory churn, and rule-evaluation latency in Grafana, letting SREs correlate user complaints with pipeline health in real time. That transparency shifted the culture from reactive war-room scrambles to proactive capacity planning: peak holiday shopping now triggers automated autoscaling rather than frantic hardware requisitions. 'Implementing enterprise streaming taught me that you can't optimize what you can't witness,' he tells colleagues. 'Sustainable speed emerges only when telemetry, not adrenaline, guides the throttle.' Engineering Cloud-Native Scalability Without Sacrificing Certainty Bhaskar's cloud playbook rejects blanket migration mantras in favour of selective control. In the CDP, transient Spark pools absorb promotional surges, yet deterministic SQL pools guard finance aggregates whose quarter-close deadlines brook no jitter. For legacy Teradata marts, he orchestrated phased lifts: first replicating tables to Delta Lake, then throttling dual-writes until cost-to-serve validated the switchover. The outcome: a 28 percent infrastructure-spend reduction and 40 percent faster analytics refresh, achieved without a single missed service-level objective. Key to that success is Bhaskar's ' blue-green schema ' principle. Every breaking change spawns a parallel dataset with full lineage tags, letting analysts A/B test queries while pipelines stabilize. Confluence runbooks codify cut-over timing, rollback triggers, and owner sign-offs—documents that slash onboarding time for new engineers and expand institutional memory. 'Years of leading cloud migrations convinced me that governance is not overhead; it is the receipt that lets leadership spend an insight with confidence,' he remarks, framing compliance as a value multiplier rather than a speed bump. Building Governance and Trust into Every Byte In Bhaskar's view, data quality is inseparable from business credibility. His Python validation suite—deployed across finance, loyalty, and operations marts—executes column-level null-rate thresholds, referential integrity checks, and PII redaction before write-ahead logs commit. Alerting hooks into Microsoft Teams channel anomalies to domain owners, turning data stewardship from a back-office chore into a shared muscle. He extends that ethos to security. Field-level encryption keys rotate via Azure Key Vault every 30 minutes; privilege boundaries mirror the company's zero-trust macros; and access reviews auto-generate diff-reports for identity teams. When a hospitality subsidiary requested GDPR alignment, Bhaskar's lineage mapping shaved the remediation estimate by half because sensitive fields were already traceable to their ingress points. Finally, he threads ethical guardrails into machine-learning workflows. Model cards declare data provenance, training drift metrics, and fairness audits. Deployment pipelines block models lacking bias attestations, preventing ' shadow AI ' from reaching production. That diligence not only appeases regulators but also lends the brand a transparency halo coveted in the loyalty space. Mentoring Engineers and Business Stakeholders Alike Bhaskar multiplies impact by elevating those around him. A 12-week rotation cycles new hires through ingestion, transformation, and visualization squads; shadow commits mature into lead features by week nine. Attrition among graduates hovers below five percent, a stat HR attributes to his scaffolded learning path. Yet mentorship extends beyond engineers. Product owners attend ' data-design studios ' where white-boarded user stories receive lineage annotations in real time. Finance analysts preview SQL plans before rollout, ensuring metric alignment. And quarterly 'pipeline retros' invite marketing, risk, and compliance to grade data freshness, defect rates, and analytic adoption—an exercise that has lifted cross-team Net Promoter Scores by 18 points in two years. The payoff surfaces during audits and performance reviews alike. Engineers present stack decisions in business vernacular, while executives reference lineage dashboards confidently in board decks. The cultural dividend: data ceases to be an IT asset and becomes a lingua franca that knits departments together. AI-Driven Horizons: Closing the Loop from Creation to Consequence Looking forward, Bhaskar envisions large-language-model copilots absorbing descriptive analytics so humans can hunt causal insight. He is already piloting a retrieval-augmented generation layer that condenses campaign metrics into prose directly inside the CRM, cutting weekly review prep from four hours to fifteen minutes. Beneath that convenience sits a rigorous ethics stack. Differential privacy guards low-cardinality segments; synthetic data augments sparse classes; and bias scans flank model re-training. The coming wave, Bhaskar predicts, is vector databases that store behavioural embeddings alongside transactional facts, powering room-upgrade nudges the moment a guest slows at a lobby kiosk. But precision must never outpace permission: consent flags propagate through Kafka headers, ensuring downstream models honour opt-outs instantly. 'Scaling machine learning has shown me that relevance without trust is thin ice; only when transparency walks in lockstep with automation does AI create durable value,' Bhaskar contends, summarizing a philosophy that weds innovation to accountability.
