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Can Snowflake Be Utilized for Data Lakes

Can Snowflake Be Utilized for Data Lakes

Can Snowflake Be Utilized for Data Lakes? Snowflake’s cloud-native architecture offers significant advantages for enhancing data lakes. By integrating various architectural patterns, Snowflake simplifies the creation and management of data lakes, enabling organizations to fully capitalize on their data assets. Here’s why Snowflake is an ideal solution for data lakes: Typical Steps in Building a Data Lake: Does Snowflake Utilize AWS or Azure? In Snowflake, an “external stage” refers to a location outside its own storage where data files can be kept. Both AWS and Azure can be utilized as external stages in Snowflake, offering flexibility in data storage options. Snowflake for Data Lakes: Snowflake on Azure for Data Lakes: For Microsoft Azure users, Snowflake delivers performance, security, and seamless management. Integration with Azure Data Factory (ADF) enhances data ingestion and querying capabilities within Snowflake. Why Choose Snowflake for Data Lakes? Success Stories: Siemens: Transitioning from a large on-premises SAP HANA data lake to Snowflake allowed Siemens to overcome scaling issues and integrate AI solutions more effectively. Christian Meyer, Head of Cloud Operations and Chief Technology Architect at Siemens AG, noted the challenge of scaling and integrating diverse data types and the benefit of separating storage and compute to control costs. Bumble Inc.: Using Snowflake as a unified platform for data warehousing, business intelligence, and data lakes, Bumble democratized data access, enhanced collaboration, and fostered innovation. Head of Data Vladimir Kazanov highlighted that Snowflake addressed the limitations of their legacy data warehouse, improving reporting consistency and efficiency. Snowflake’s capabilities make it a powerful tool for managing data lakes, offering flexibility, efficiency, and scalability for organizations across various industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Unlocking Enterprise AI Success

Unlocking Enterprise AI Success

Companies are diving into artificial intelligence. Unlocking enterprise AI success depends on four main factors. Tectonic is here to help you address each. Trust is Important-Trust is Everything Data is everything—it’s reshaping business models and steering the world through health and economic challenges. But data alone isn’t enough; in fact, it can be worse than useless—it’s a risk unless it’s trustworthy. The solution lies in a data trust strategy: one that maximizes data’s potential to create value while minimizing the risks associated with it. Data Trust is Declining, Not Improving Do you believe your company is making its data and data practices more trustworthy? If so, you’re in line with most business leaders. However, there’s a disconnect: consumers don’t share this belief. While 55% of business leaders think consumers trust them with data more than they did two years ago, only 21% of consumers report increased trust in how companies use their data. In fact, 28% say their trust has decreased, and a staggering 76% of global consumers view sharing their data with companies as a “necessary evil.” For companies that manage to build trust in their data, the benefits are substantial. Yet, only 37% of companies with a formal data valuation process involve privacy teams. Integrating privacy is just one aspect of building data trust, but companies that do so are already more than twice as likely as their peers to report returns on investment from key data-driven initiatives, such as developing new products and services, enhancing workforce effectiveness, and optimizing business operations. To truly excel, companies need to create an ongoing system that continually transforms raw information into trusted, business-critical data. Data is the Backbone-Data is the Key Data leaks, as shown below, are a major factor on data trust and quality. As bad as leaked data is to security, data availability is to being a data-driven organization. Extortionist Attack on Costa Rican Government Agencies In an unprecedented event in April 2022, the extortionist group Conti launched a cyberattack on Costa Rican government agencies, demanding a $20 million ransom. The attack crippled much of the country’s IT infrastructure, leading to a declared state of emergency. Lapsus$ Attacks on Okta, Nvidia, Microsoft, Samsung, and Other Companies The Lapsus$ group targeted several major IT companies in 2022, including Okta, Nvidia, Microsoft, and Samsung. Earlier in the year, Okta, known for its account and access management solutions—including multi-factor authentication—was breached. Attack on Swissport International Swissport International, a Swiss provider of air cargo and ground handling services operating at 310 airports across 50 countries, was hit by ransomware. The attack caused numerous flight delays and resulted in the theft of 1.6 TB of data, highlighting the severe consequences of such breaches on global logistics. Attack on Vodafone Portugal Vodafone Portugal, a major telecommunications operator, suffered a cyberattack that disrupted services nationwide, affecting 4G and 5G networks, SMS messaging, and TV services. With over 4 million cellular subscribers and 3.4 million internet users, the impact was widespread across Portugal. Data Leak of Indonesian Citizens In a massive breach, an archive containing data on 105 million Indonesian citizens—about 40% of the country’s population—was put up for sale on a dark web forum. The data, believed to have been stolen from the “General Election Commission,” included full names, birth dates, and other personal information. The Critical Importance of Accurate Data There’s no shortage of maxims emphasizing how data has become one of the most vital resources for businesses and organizations. At Tectonic, we agree that the best decisions are driven by accurate and relevant data. However, we also caution that simply having more data doesn’t necessarily lead to better decision-making. In fact, we argue that data accuracy is far more important than data abundance. Making decisions based on incorrect or irrelevant data is often worse than having too little of the right data. This is why accurate data is crucial, and we’ll explore this concept further in the following sections. Accurate data is information that truly reflects reality or another source of truth. It can be tested against facts or evidence to verify that it represents something as it actually is, such as a person’s contact details or a location’s coordinates. Accuracy is often confused with precision, but they are distinct concepts. Precision refers to how consistent or varied values are relative to one another, typically measured against some other variable. Thus, data can be accurate, precise, both, or neither. Another key factor in data accuracy is the time elapsed between when data is produced and when it is collected and used. The shorter this time frame, the more likely the data is to be accurate. As modern businesses integrate data into more aspects of their operations, they stand to gain significant competitive advantages if done correctly. However, this also means there’s more at stake if the data is inaccurate. The following points will highlight why accurate data is critical to various facets of your company. Ease and speed of access Access speeds are measured in bytes per second (Bps). Slower devices operate in thousands of Bps (kBps), while faster devices can reach millions of Bps (MBps). For example, a hard drive can read and write data at speeds of 300MBps, which is 5,000 times faster than a floppy disk! Fast data refers to data in motion, streaming into applications and computing environments from countless endpoints—ranging from mobile devices and sensor networks to financial transactions, stock tick feeds, logs, retail systems, and telco call routing and authorization systems. Improving data access speeds can significantly enhance operational efficiency by providing timely and accurate data to stakeholders throughout an organization. This can streamline business processes, reduce costs, and boost productivity. However, data access is not just about retrieving information. It plays a crucial role in ensuring data integrity, security, and regulatory compliance. Effective data access strategies help organizations safeguard sensitive information from unauthorized access while making it readily available to those who are authorized. Additionally, the accuracy and availability of data are essential to prevent data

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Salesforce Data Cloud Pioneer

Salesforce Data Cloud Pioneer

While many organizations are still building their data platforms, Salesforce Data Cloud Pioneer has made a significant leap forward. By seamlessly incorporating metadata integration, Salesforce has transformed the modern data stack into a comprehensive application platform known as the Einstein 1 Platform. Led by Muralidhar Krishnaprasad, executive vice president of engineering at Salesforce, the Einstein 1 Platform is built on the company’s metadata framework. This platform harmonizes metadata and integrates it with AI and automation, marking a new era of data utilization. The Einstein 1 Platform: Innovations and Capabilities Salesforce’s goal with the Einstein 1 Platform is to empower all business users—salespeople, service engineers, marketers, and analysts—to access, use, and act on all their data, regardless of its location, according to Krishnaprasad. The open, extensible platform not only unlocks trapped data but also equips organizations with generative AI functionality, enabling personalized experiences for employees and customers. “Analytics is very important to know how your business is doing, but you also want to make sure all that data and insights are actionable,” Krishnaprasad said. “Our goal is to blend AI, automation, and analytics together, with the metadata layer being the secret sauce.” Salesforce Data Cloud Pioneer In a conversation with George Gilbert, senior analyst at theCUBE Research, Krishnaprasad discussed the platform’s metadata integration, open-API technology, and key features. They explored how its extensibility and interoperability enhance usability across various data formats and sources. Metadata Integration: Accommodating Any IT Environment The Einstein 1 Platform is built on Trino, the federated open-source query engine, and Spark for data processing. It offers a rich set of connectors and an open, extensible environment, enabling organizations to share data between warehouses, lake houses, and other systems. “We use a hyper-engine for sub-second response times in Tableau and other data explorations,” Krishnaprasad explained. “This in-memory overlap engine ensures efficient data processing.” The platform supports various machine learning options and allows users to integrate their own large language models. Whether using Salesforce Einstein, Databricks, Vertex, SageMaker, or other solutions, users can operate without copying data. The platform includes three levels of extensibility, enabling organizations to standardize and extend their customer journey models. Users can start with basic reference models, customize them, and then generate insights, including AI-driven insights. Finally, they can introduce their own functions or triggers to act on these insights. The platform continuously performs unification, allowing users to create different unified graphs based on their needs. “We’re a multimodal system, considering your entire customer journey,” Krishnaprasad said. “We provide flexibility at all levels of the stack to create the right experience for your business.” The Triad of AI, Automation, and Analytics The platform’s foundation ingests, harmonizes, and unifies data, resulting in a standardized metadata model that offers a 360-degree view of customer interactions. This approach unlocks siloed data, much of which is in unstructured forms like conversations, documents, emails, audio, and video. “What we’ve done with this customer 360-degree model is to use unified data to generate insights and make these accessible across application surfaces, enabling reactions to these insights,” Krishnaprasad said. “This unlocks a comprehensive customer journey.” For instance, when a customer views an ad and visits the website, salespeople know what they’re interested in, service personnel understand their concerns, and analysts have the information needed for business insights. These capabilities enhance customer engagement. “Couple this with generative AI, and we enable a lot of self-service,” Krishnaprasad added. “We aim to provide accurate answers, elevating data to create a unified model and powering a unified experience across the entire customer journey.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Data Cloud - Facts and Fiction

Data Cloud – Facts and Fiction

Salesforce Data Cloud: Debunking Myths and Unveiling Facts If you’ve been active on LinkedIn, attending recent Salesforce events, or even watching a myriad of sporting events, you’ve likely noticed that Salesforce has evolved beyond just CRM. It’s now CRM + DATA + AI. Although Salesforce has always incorporated these elements, with Einstein AI and data being integral to CRM, the latest innovation lies in the Data Cloud. Data Cloud – Facts and Fiction Data Cloud, formerly known as Salesforce Genie, represents Salesforce’s latest evolution, focusing on enabling organizations to scale and grow in an era where data is the new currency. It is the fastest-growing product in Salesforce’s history, pushing new boundaries of innovation by providing better access to data and actionable insights. As Data Cloud rapidly develops, potential clients often have questions about its function and how it can address their challenges. Here are some common myths about Data Cloud and the facts that debunk them. Myth: Data Cloud Requires MuleSoft Fact: While MuleSoft Anypoint Platform can accelerate connecting commonly used data sources, it is not required for Data Cloud. Data Cloud can ingest data from multiple systems and platforms using several out-of-the-box (OOTB) connectors, including SFTPs, Snowflake, AWS, and more. Salesforce designs its solutions to work seamlessly together, but Data Cloud also offers connector options for non-Salesforce products, ensuring flexibility and integration capabilities beyond the Salesforce ecosystem. Myth: Data Cloud Will De-Duplicate Your Data Fact: Harmonizing data in Data Cloud means standardizing your data model rather than de-duplicating it. Data Cloud maps fields to a common data model and performs “Identity Resolution,” using rules to match individuals based on attributes like email, address, device ID, or phone number. This process creates a Unified Individual ID without automatically de-duplicating Salesforce records. Salesforce intentionally does not function as a Master Data Management (MDM) system. Myth: Data Cloud Will Create a Golden Record Fact: Data Cloud does not create a single, updated record synchronized across all systems (a “golden record”). Instead, it retains original source information, identifies matches across systems, and uses this data to facilitate engagements, known as the Data Cloud Key Ring. For instance, it can recognize an individual across different systems and provide personalized experiences without overwriting original data. Myth: You Can’t Ingest Custom Objects from Salesforce Fact: During the data ingestion process, you can select which objects to ingest from your Salesforce CRM Org, including custom objects. The system identifies the API names of the objects and fields from the data source. Ensuring the Data Cloud integration user has access to the necessary information (similar to assigning Permission Sets) allows you to ingest and map custom objects accordingly. Myth: Data Cloud Requires a Data Scientist and Takes a Long Time to Implement Fact: While implementing Data Cloud involves ingesting, mapping data, running identity resolution, and generating insights, it does not necessarily require a data scientist. Skilled Salesforce Admins can often manage data integration from third-party applications. Effective Data Cloud implementation requires thorough planning and preparation, akin to prepping a room before painting. Identifying use cases and understanding data sources in advance can streamline the implementation process. Myth: Data Cloud is Expensive Fact: Data Cloud operates on a consumption-based pricing model. Engaging in strategic conversations with Salesforce Account Executives can help understand the financial implications. Emphasizing the value of a comprehensive data strategy and considering the five V’s of Big Data—Volume, Variety, Veracity, Value, and Velocity—ensures that your data supports meaningful business outcomes and KPIs. In Summary Salesforce Data Cloud represents a significant evolution in managing and leveraging data within your organization. It helps break down data silos, providing actionable insights to drive organizational goals. Despite initial misconceptions, implementing Data Cloud does not require extensive coding skills or a data scientist. Instead, thorough planning and preparation can streamline the process and maximize efficiency. Understanding the value of a comprehensive data strategy is crucial, as data becomes the new currency. Addressing the five V’s of Big Data ensures that your data supports meaningful business outcomes and KPIs. At Tectonic, our team of certified professionals is ready to assist you on this journey. We offer a Salesforce Implementation Solution package to help you get hands-on with the tool and explore its capabilities. Whether you need help understanding your data sources or defining use cases, our data practice can provide the expertise you need. Talk to Tectonic about Data Cloud and discover how our tailored solutions can help you harness the full potential of your data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Data Foundation

AI Data Foundation

In the era of AI, the Data Foundation is crucial for empowering AI-driven customer experiences. Data Cloud emerges as a unifying force, seamlessly integrating data to fuel transformative AI encounters and elevate customer-centricity. Beyond mere data management, Data Cloud represents a significant advancement, enabling profound insights by harmonizing diverse data sources with CRM data from the Salesforce platform. This convergence facilitates the unlocking of actionable insights critical for informed decision-making. In a strategic collaboration, Salesforce and AWS extend their partnership to enhance AI capabilities. AWS AI services are integrated into Salesforce’s Einstein Trust Layer, empowering Data Cloud with seamless access to AWS data services and compute resources. Additionally, Data Cloud and other Salesforce offerings are now accessible through the AWS Marketplace, streamlining procurement processes. This insight explores how Data Cloud unifies vast and varied business data with CRM data from the Salesforce Einstein Platform. It serves as a robust foundation for AI-powered customer experiences, providing businesses with unprecedented insights into their data universe. With Data Cloud, businesses can seamlessly combine CRM data with diverse sources, including transactional data, IoT device data, and social media interactions. This consolidation fosters a single source of truth, enhancing decision-making and the relevance of AI models. Unlike traditional approaches that involve laborious data movement, Data Cloud operates on AWS infrastructure, enabling seamless data connectivity and preparation without the need for ETL processes. Leveraging Apache Iceberg and Salesforce’s contributions, Data Cloud ensures data consistency, flexibility, and interoperability, essential for AI-driven insights. Moreover, Data Graphs offer a novel approach to assemble and rapidly access data collections from disparate sources, facilitating grounded AI experiences. Through Model Builder and Einstein Copilot Studio, businesses can seamlessly access Data Cloud data in Amazon SageMaker for custom AI model creation without ETL overheads. This partnership between Salesforce and AWS represents a paradigm shift in data management and AI integration. By combining Salesforce’s customer-centric approach with AWS’s scalable infrastructure, Data Cloud empowers businesses to harness AI as a practical tool for growth and innovation in the digital era. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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