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Many businesses rush to adopt AI but fail due to poor strategy. This post serves as your go-to playbook for success.View the full article

Most organizations find it challenging to manage data from diverse sources efficiently. Amazon Web Services (AWS) enables you to address this challenge with Amazon RDS, a scalable relational database service for Microsoft SQL Server (MS SQL). However, simply storing the data isn’t enough. To drive your business growth, you need to analyze this data to […]View the full article

Good quality data is the holy grail, and that’s what you should always aim for. But that saying goes incomplete without data models. While all of us know the importance of data, profits or sales turn in only when organizations know how to find, model, track and understand their data appropriately. Data modeling, being one […]View the full article

Google has always pioneered the development of large and scalable infrastructure to support its search engine and other products. Its vast network servers have enabled us to store and manage immense volumes of data. As cloud computing gained notoriety, Google expanded its operations and launched Google Cloud Platform (GCP). The Google Cloud Storage (GCS) allows […]View the full article

Similar to the iterative nature of AI projects, AI strategy also requires continuous adjustments to bring successful AI transformation.View the full article

Agile processes, prioritizing speed, quality, and efficiency, are gradually replacing traditional software development and deployment methods in the age of rapid software delivery. CI/CD has become a fundamental component of contemporary software development, allowing teams to automate the processes of building, testing, and deploying software. The pipeline facilitates the flow of code changes from development to production and is the central component of continuous integration and delivery (CI/CD). Recent studies like “State of DevOps” highlight that the most common DevOps practices are continuous integration (CI) and continuous delivery (CD), used by 80% of organizations. In addition to speeding up software delivery, automating the CI/CD process improves product quality by enabling frequent testing and feedback loops. However, rigorous preparation, strategy, and adherence to best practices are necessary for implementing efficient automation. This article explores 7 essential tactics and industry best practices for CI/CD pipeline automation. 1. Infrastructure as Code (IaC) Treating infrastructure like code is a cornerstone of contemporary DevOps methods. By putting infrastructure needs into code, teams may achieve consistency, reproducibility, and scalability in their environments. IaC tools like Terraform, CloudFormation, or Ansible are crucial for automating CI/CD pipelines. Strategy 1: Use declarative code to define the infrastructure needs for provisioning and configuring CI/CD pipeline resources, including deployment targets, build servers and testing environments. Best Practice: To preserve version history and facilitate teamwork, save infrastructure code in version control repositories with application code. 2. Containerization Containerization has completely transformed software packaging and deployment, as best demonstrated by platforms like Docker. Containers encapsulate an application and its dependencies, providing consistency between environments. Using containerization in CI/CD pipeline automation facilitates smooth deployment and portability. Strategy 2: As part of the continuous integration process, build Docker images to produce lightweight, portable artifacts that can be reliably deployed in a range of contexts. Best Practice: Use container orchestration systems such as Kubernetes to manage containerized applications in production and ensure scalability, robustness, and ease of deployment. 3. Test Automation One of the main components of CI/CD is automated testing, which helps teams to validate code changes quickly and accurately. Teams can reduce the risk of regressions and ensure software quality by automating tests at different stages of the development cycle, such as unit, acceptance, integration, and so on, and catching errors early. Strategy 3: Include automated tests in the continuous integration pipeline to verify code changes immediately after each contribution, giving engineers quick feedback. Best Practice: The best practice is to use a test pyramid method where speed and coverage are optimized by starting with more unit tests that run quickly and moving up to fewer but more comprehensive integration and end-to-end tests at higher levels. 4. Infrastructure Monitoring and Optimization Continuous monitoring of infrastructure performance is crucial for maintaining the reliability and efficiency of CI/CD pipelines. By leveraging monitoring tools such as Prometheus or Datadog, we can track resource utilization, identify issues, and optimize infrastructure configurations to enhance pipeline performance. Strategy 4: Implement automated infrastructure monitoring to track key performance metrics such as CPU usage, memory consumption, and network traffic, enabling proactive identification and resolution of issues that may impact performance. Best Practice: Utilize alerting mechanisms to notify teams of abnormal infrastructure behavior or performance degradation, facilitating rapid response and minimizing downtime in CI/CD pipelines. 5. Security Automation and Compliance Security is a paramount concern in any system, and integrating security practices into CI/CD pipelines is essential for mitigating risks and ensuring regulatory compliance. By automating security checks and compliance audits using tools like SonarQube or OWASP ZAP, we can detect vulnerabilities early in the development lifecycle and enforce security standards consistently. Strategy 5: Embed security scans and compliance checks into the CI/CD pipeline to automatically assess code quality, identify security vulnerabilities, and enforce security policies throughout the software delivery process. Best Practice: Integrate security testing tools with version control systems such as Jenkins or Git to perform automated code analysis on every commit, enabling developers to address security issues promptly and maintain a secure codebase. 6. Monitoring and Feedback Pipelines for continuous integration and delivery (CI/CD) are essential because they offer insight into the functionality and state of deployed applications. Teams may find bottlenecks, spot anomalies, and continuously increase the efficiency of the pipeline by gathering and evaluating metrics. Strategy 6: Use infrastructure and apps to record pertinent metrics and logs, allowing for proactive monitoring and troubleshooting. Best Practice: To guarantee that deployed applications fulfill performance and reliability requirements, incorporate monitoring and alerting technologies into the CI/CD pipeline. This will allow the pipeline to detect and respond to issues automatically. 7. Infrastructure Orchestration Orchestration is essential to automating CI/CD pipelines and controlling infrastructure as code. Delivery workflows are ensured by orchestration technologies such as Jenkins, CircleCI, or GitLab CI/CD, which facilitate the execution of pipeline steps, manage dependencies, and coordinate parallel operations. Strategy 7: Use CI/CD orchestration technologies to automate pipeline steps, such as code compilation, testing, deployment, and release, to coordinate complicated workflows smoothly. Best Practice: Defining pipeline stages and dependencies explicitly, optimizing execution order and resource use, and minimizing build times can foster a highly efficient CI/CD environment. Conclusion Automation lies at the core of successful CI/CD pipelines, enabling teams to deliver high-quality software quickly and at scale. By adopting the strategies and best practices outlined in this article, organizations can streamline their development workflows, reduce manual overhead, and foster a culture of collaboration and continuous improvement. Embracing automation accelerates time-to-market and enhances software delivery’s resilience, reliability, and overall quality in today’s fast-paced digital landscape. The post 7 Strategies and Best Practices for Automating CI/CD Pipelines appeared first on Amazic. View the full article

Preemptive protection and reactive cybersecurity strategies for best possible ransomware protection We live in a time where digital transformation dictates the pace of business, and the necessity for ransomware protection strategies and preemptive protection is essential to organizational integrity and continuity. “Ransomware will cost its victims around $265 billion (USD) annually by 2031, with a... The post Proactive and Reactive Ransomware Protection Strategies appeared first on TrueFort. The post Proactive and Reactive Ransomware Protection Strategies appeared first on Security Boulevard. View the full article

A company’s data strategy is always in motion. Since the explosion of interest in generative AI and large language models (LLMs), that is more true than ever, with business leaders discussing how quickly they should adopt these technologies to stay competitive. Some emerging approaches may be seen in our newly released Snowflake Data Trends 2024, looking at how users in the Data Cloud are working with their data. Understanding which features, languages and approaches our users embrace provides real indications of how organizations are preparing their data for this fast-moving new age of advanced AI. For the report, we focused on two broad aspects of data strategy. We looked at activity around LLMs and the applications that work with them. But before a company can be successful with generative AI, LLMs and other innovative technologies, it has to build a strong data foundation. So we first looked at foundational activities that could suggest whether organizations are shifting or accelerating the work of preparing for advanced new technologies. We saw that collectively, organizations are definitely preparing their data to be used more effectively with powerful, new AI technologies. The most marked finding was around governance. Strong data governance is essential to meet security and compliance obligations, but it is often regarded as a hindrance. If IT “locks down” the data, it can’t be used to derive insight and refine strategy — or so the complaint often goes. Looking at activity in the Data Cloud, we found quite the opposite. Application of individual governance features (tags applied to data objects, masking policies assigned to data sets, etc.) rose between 72% and 98% from January 2023 to January 2024. The cumulative number of queries run against this policy-protected data rose as well, by 142%. This suggests that even as organizations increase the granularity of their data governance practices, they’re able to do more, not less, with the data. The report also covers increased usage of AI-friendly programming language Python, which grew by 571%. We also saw a lot more work with unstructured data, which has great AI potential, since estimates consistently put the share of all data that’s unstructured at 80% to 90%. Looking at AI work and applications, our most exciting activity was from our Streamlit developer community. From April 2023 to January 2024, more than 20,000 developers worked on 33,000+ LLM applications, including apps in development. Across that period, the percentage of such apps that were chatbots increased notably, from 18% in April to 46% by the end of January. We would expect much of this to have been experimentation and pilot projects, but the fact that so many devs are eager to work with these complex AI models only confirms the expectation that a transformative wave of innovation is beginning. Further insights into the potential shape of the future: The fast-growing adoption of the Snowflake Native App Framework (generally available on AWS and Azure, private preview on GCP), since it entered public preview last summer, tells us that our mantra of bringing computation to a single, secure set of data — rather than farming out copies of your data to various environments — resonates with our users. When we look at how the features of the Data Cloud are actually being used, we not only refine our own product plans, we also see the trends and the future of enterprise data. We continue to innovate at Snowflake to shape that future. I hope that this report gives business and IT leaders ideas and indicators that will help them shape their data strategies within the Data Cloud and beyond. For more, read Snowflake Data Trends 2024. The post Data Trends 2024: Strategies for an AI-Ready Data Foundation appeared first on Snowflake. View the full article

Last year, we held our first Accelerate event, to explore industry trends, data and technology innovations, and data strategy case studies in financial services. This year, we are expanding to five industry events, featuring leaders in financial services; retail and consumer goods; manufacturing; media, advertising and entertainment; and healthcare and life sciences. Accelerate Financial Services and Accelerate Retail are one-day virtual events brought to you by Microsoft. Join technology and business leaders from Microsoft, Bloomberg, Goldman Sachs and more to discover executive priorities, best practices, and potential data and AI challenges that are top of mind for 2024. Why Attend Accelerate Financial Services? Accelerate Financial Services is a virtual event on March 14, 2024, starting at 11 a.m. PT/2 p.m. ET. It will explore how industry-leading organizations are building data, apps and AI strategies in the Data Cloud. Come to hear an insightful keynote and executive session, and attend deep dives featuring customer stories as well as product demos for AI use cases and apps. Agenda highlights include opportunities to: Hear from industry leaders: Rinesh Patel, Snowflake’s Global Head of Financial Services, will deliver a keynote on how the Financial Services Data Cloud will accelerate your data and AI transformation initiatives. Simplify the data foundation: Explore how you can simplify your data foundation by bringing your data together onto a single platform, enhancing security and governance controls, and thinking about connectivity and resilience. Hear directly from Sunil Mathews, VP of Enterprise Data & Analytics at Franklin Templeton, on how the asset management firm built its Integrated Data Hub on Snowflake to accelerate decision-making across the enterprise. Scale your business with applications: Hear about how industry-leading organizations are turning to Snowflake to help translate their data into business and commercial outcomes through native apps. Check out demos from Goldman Sachs and Bloomberg on the Goldman Sachs Legend and Bloomberg Data License native apps, respectively. Accelerate AI success with Snowflake: Learn how organizations are building AI strategies with cloud and enterprise data initiatives, and where Snowflake fits into this journey. Hear about the key business use cases applied with generative AI and where the industry is investing and applying this technology over the next three to five years. Join the financial services executive panel: Learn what are top priorities for financial services executives, what business and technology challenges they’re looking to solve, how they have partnered with Snowflake, and where they see the industry moving to in the future. The panel features Sean Foley, CTO of Worldwide Financial Services at Microsoft, and Spiros Giannaros, Executive Vice President, CEO and President of Charles River Development. And to learn more about the latest data and AI trends in the industry, read our new report, Data Trends 2024: Financial Services. Why Attend Accelerate Retail? Accelerate Retail is a virtual event on May 2, 2024 at 11 a.m. PT/2 p.m. ET. Agenda highlights include opportunities to: Hear from industry leaders: Prabhath Nanisetty, Industry Principal for Retail Data & Quick Commerce at Snowflake, will kick off the event with a keynote on how organizations around the globe are accelerating data and AI transformation initiatives with the Retail Data Cloud. In the executive panel session, hear directly from Rosemary DeAragon, Snowflake’s global GTM Lead for Retail and Consumer Goods, Shanthi Rajagopalan, Global Head of Strategy for Retail and Consumer Goods at Microsoft, Pat Nestor, Global Head of Analytics Products at Kraft Heinz, and Alex Izydorczyk, Founder and CEO at Cybersyn, a Snowflake affiliate, on what is top of mind in the era of data transformation and how they have partnered with Snowflake to solve today’s business challenges. Simplify the data foundation: As retail organizations look to adopt new technologies like generative AI or implement their transformation agendas, a robust enterprise data strategy is becoming more critical than ever. Trevor Kaplan, senior sales engineer at Snowflake, will delve into the data capabilities required for success, and Pat Nestor, Global Head of Analytics Products at Kraft Heinz, will share how the brand simplified their data foundation with Snowflake. Scale your business with applications: Today’s leading retail and consumer goods organizations build applications to generate insights, delight customers and optimize operations. Listen to the latest innovation enabling businesses to build, deploy and deliver value from apps, presented by Brian Stanley, senior sales engineer at Snowflake. Watch live demos from Snowflake partners Crisp, LiveRamp and Cybersyn to learn how to derive insights from data by leveraging native apps in Snowflake Marketplace. Accelerate AI success with Snowflake: Discover how organizations are building AI strategies with cloud and enterprise data initiatives, and where Snowflake fits into this transformation journey. Valentine Fontama, Senior Principal Sales Engineer, Data Science at Snowflake, will demonstrate how to build and host a large language model in Snowflake. Learn to build interactive chat applications with Snowflake, and quickly and securely bring AI to the data with Snowpark Container Services, now in public preview, and Cortex Copilot, now in private preview. About the Microsoft and Snowflake partnership Snowflake on Microsoft Azure is a best-in-class AI-powered Data Cloud with robust integration with the Microsoft cloud. The Snowflake Data Cloud on Azure provides a path to practical AI-enhanced data strategy, de-siloing and protecting enterprise data built on the Azure platform, which enables secure data mobilization for intensive workloads such as warehousing, machine learning, generative AI and app development. Snowflake on Microsoft Azure paves the way to a unified data strategy that applies AI as leverage for business goals, adding secure data collaboration, removing friction, and accelerating data migration while improving governance, development, analytics and improved customer experiences — all using your enterprise data combined with the power of Microsoft Azure. To learn more about Snowflake’s evolving partnership with Microsoft, watch this video featuring Frank Slootman, Snowflake’s Chairman and former CEO, and Satya Nadella, Microsoft’s Chairman and CEO. Ready to Accelerate? Register now to reserve your spot for Accelerate Financial Services and Accelerate Retail. Or download the new research report, Data Trends 2024: Financial Services. The post How Financial Services and Retail Companies Are Accelerating their Data, Apps and AI Strategy in the Data Cloud appeared first on Snowflake. View the full article

In today's hyper-connected world, data is often likened to the new oil—a resource that powers modern businesses. As organizations expand their operational landscapes to leverage the unique capabilities offered by various cloud service providers, the concept of a multi-cloud strategy is gaining traction. However, the real power of a multi-cloud approach lies in the ability to seamlessly integrate data across these diverse platforms. Without effective data integration, a multi-cloud strategy risks becoming a siloed, inefficient operation. This blog post aims to explore the complexities and solutions surrounding data integration in multi-cloud environments. We will delve into the different strategies organizations can employ, from API-based integrations to event-driven architectures, while also addressing the elephant in the room—security concerns and how to mitigate them... View the full article

Speed has always been one of the key metrics of business success. The need for speed has been amplified in recent years, with time-to-market, streamlined production, seamless logistics, and round-the-clock customer service all now expected as a baseline for companies that want to compete. We’ve reached the limit of what hard work and elbow grease can achieve in these fields, and that’s where artificial intelligence (AI) takes the baton and runs with it... View the full article

Any development process must include the deployment of new software versions or features. It does, however, present risks and uncertainties, making it a daunting task. The user experience and system disruption caused by new releases are things that organizations work to prevent. Canary releases become important at this point. Canary releases provide a controlled and gradual method of rolling out software updates, reducing risks and obtaining crucial feedback prior to full-scale rollout. In this article, we will explore the concept of canary releases, their benefits, and best practices for implementing them. View the full article

Customers can have numerous data analytics teams within a single organization that each require workflow or data pipeline orchestration. It is important to evaluate the tenancy design of your implementation to improve the efficiency, scalability, and security of your organization. Google Cloud offers Cloud Composer, a fully managed workflow orchestration service built on Apache Airflow offering end-to-end integration with Google Cloud products including BigQuery, Dataflow, Dataproc, Datastore, Cloud Storage, Pub/Sub, and Vertex AI. This guide compares the pros and cons of different tenancy strategies for Cloud Composer. We’ll evaluate the differences between a multi-tenant Composer strategy versus a single-tenant Composer strategy. In other words, a single shared Composer environment for all data teams vs. a Composer environment for each data team... View the full article

Why AI has everyone’s attention, what it means for different data roles, and how Alteryx and Snowflake are bringing AI to data use cases LLaMA (Large Language Model Meta AI), along with other large language models (LLMs) have suddenly become more open and accessible for everyday applications. Ahmad Khan, Head of artificial intelligence (AI) machine learning (ML) strategy at Snowflake, has not only witnessed the emergence of AI in the data space but helped shape it throughout his career. At Snowflake, Ahmad works with customers to solve AI use cases and helps define the product strategy and vision for AI innovation, which includes key technology partners like Alteryx. Read on for the interview highlights and listen to the podcast episode for the full conversation. View the full article

As technologies and business priorities change, so should your data mobility strategy, regardless of business size. View the full article

Platform engineering helps centralize the responsibilities associated with maintaining internal infrastructure. And it continues to be an emerging discipline—according to the 2023 State of DevOps Report, 51% of organizations have already adopted platform engineering within the last three years, and 93% said it’s a step in the right direction. I recently met with Hope Lynch, […] The post Strategies To Consider When Adopting Platform Engineering appeared first on DevOps.com. View the full article

Planview this week announced it completed its acquisition of Tasktop, a provider of a value stream management (VSM) platform that enables DevOps teams to align their efforts more closely with strategic business goals. Louise Allen, chief product officer for Planview, said the goal is to tighten integration between the Tasktop VSM platform and the project […] The post Planview Outlines VSM Strategy After Acquiring Tasktop appeared first on DevOps.com. View the full article

With rigorous development and pre-production testing, your microservices will perform as they should. However, microservices need to be continuously tested against actual end-user activity to adapt the application to changing preferences and requests. This article will cover five deployment strategies that will help developers and DevOps teams when releasing new features or making changes to […] The post 5 Testing Strategies For Deploying Microservices appeared first on DevOps.com. View the full article

The massive shift to remote work necessitated by the COVID-19 pandemic kickstarted many companies’ digital transformation. While this transition had already begun for most organizations, the pandemic sped up the timeline. These events also had an impact on how storage and backup is done. Let’s look at why the right storage solution is key to […] The post Fast Object Storage Key to Backup and DR Strategy appeared first on DevOps.com. View the full article