Latest news with #SQL
Time of India
8 hours ago
- Business
- Time of India
Data analyst or financial analyst: Which role offers better career growth?
In today's fast-changing job market, two roles stand out for anyone who enjoys numbers, insights, and strategy: data analyst and financial analyst. Both careers are in demand, promise strong salaries, and put you at the center of decision-making. But if you're wondering which path offers better long-term career growth, the answer isn't as simple as one being "better" than the other. It all depends on your interests, strengths, and how you want to grow. Let's break it down—what each role involves, what kind of person thrives in it, and where it can take you in five or ten years. Financial Analyst A financial analyst is responsible for evaluating a company's financial data to assist with decision-making related to budgeting, investment, and long-term planning. Their work typically involves analyzing financial statements, preparing financial models, forecasting future performance, and monitoring economic trends. Key Responsibilities: Prepare and analyze balance sheets, income statements, and cash flow reports Build financial models to project future revenue and expenses Conduct variance analysis and identify business risks Support investment decisions or strategic planning Industries: Finance, banking, corporate sectors, insurance, investment firms Data Analyst A data analyst focuses on collecting, processing, and analyzing large volumes of data to uncover patterns and trends. They work across industries and apply statistical methods and data visualization tools to provide insights that drive operational and strategic decisions. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like 8주 커리큘럼 파고다어학원 더 알아보기 Undo Key Responsibilities: Extract, clean, and organize data using tools like SQL Use Python, R, or Excel for statistical analysis Create dashboards and visual reports using Tableau or Power BI Communicate findings to stakeholders to improve business outcomes Industries: Tech, healthcare, e-commerce, logistics, government, consulting, especially in tech-driven industries where data is at the heart of every decision. How the skills can shape your path The kind of work you enjoy doing plays a huge role in which career path will feel right for you. If you love finance, economics, and tracking how companies grow and earn, financial analysis may feel more natural. On the other hand, if you enjoy working with large datasets, coding, and discovering trends hidden in raw information, then data analysis might be a better fit. Financial analysts often come from backgrounds in commerce, finance, accounting, or economics. They're usually very comfortable with Excel, financial modeling, and interpreting business reports. Data analysts tend to have academic roots in math, engineering, statistics, or computer science. They're more likely to write code, use visualization tools, and experiment with machine learning techniques. That said, there's a lot of crossover. Financial analysts today are expected to work with data tools, and data analysts often need strong business acumen. So, even if your background doesn't perfectly match one role, you can still pivot into it—with the right learning and upskilling. Comparing growth potential Both roles offer strong starting salaries and a stable career track. However, the kind of growth you experience—and how quickly you rise—can vary. Data analysts , especially those who work in tech, often see faster early-career growth. That's because tech companies value data-driven decisions, and as a result, they invest heavily in analytics teams. Data analysts can quickly progress into more technical roles like data scientist, analytics manager, or product analyst, often with pay raises that outpace traditional roles. Financial analysts may grow at a more measured pace, but their roles tend to carry more influence within corporate decision-making. Over time, financial analysts who prove their strategic thinking and business judgment often move into leadership roles. These positions come with higher salaries and decision-making power, particularly in sectors like investment banking or corporate finance. While it may take longer to reach a top role in finance, the destination can be very rewarding—especially for those aiming to become CFOs or financial directors. Which career is more in demand? The job market is expanding for both roles, but in slightly different ways. Data analytics is growing rapidly across all sectors, not just in tech. Whether it's healthcare organizations tracking patient outcomes or retail companies analyzing customer trends, data analysts are needed everywhere. The versatility of the role gives it a clear edge in terms of flexibility and industry mobility. Financial analysts, on the other hand, continue to be in strong demand in more traditional sectors like banking, corporate finance, and insurance. While they may not have as broad a footprint across every industry, they play an essential role in organizations where financial precision and investment planning are crucial. So, which role truly offers better career growth? The answer depends on how you define growth. If you're looking for speed, flexibility, and access to cutting-edge tools and industries, data analysis might take you there faster. But if you're seeking stability, influence in business decisions, and a path to executive leadership, financial analysis offers a powerful long game. The real secret is this: choose the role that fits your interests and learning style. You can always grow, pivot, and specialize later. Both careers offer a future-proof path—but the one that keeps you engaged, curious, and excited to learn will take you the farthest. Is your child ready for the careers of tomorrow? Enroll now and take advantage of our early bird offer! Spaces are limited.
Yahoo
4 days ago
- Business
- Yahoo
How a data-processing problem at Lyft became the basis for Eventual
When Eventual founders Sammy Sidhu and Jay Chia were working as software engineers at Lyft's autonomous vehicle program, they witnessed a brewing data infrastructure problem — one that would only become larger with the rise of AI. Self-driving cars produce a ton of unstructured data from 3D scans and photos to text and audio. There wasn't a tool for Lyft engineers that could understand and process all of those different types of data at the same time — and all in one place. This left engineers to piece together open source tools in a lengthy process with reliability issues. 'We had all these brilliant PhDs, brilliant folks across the industry, working on autonomous vehicles but they're spending like 80% of their time working on infrastructure rather than building their core application,' Sidhu, who is Eventual's CEO, told TechCrunch in a recent interview. 'And most of these problems that they were facing were around data infrastructure.' Sidhu and Chia helped build an internal multimodal data processing tool for Lyft. When Sidhu set out to apply to other jobs, he found interviewers kept asking him about potentially building the same data solution for their companies, and the idea behind Eventual was born. Eventual built a Python-native open source data processing engine, known as Daft, that is designed to work quickly across different modalities from text to audio and video, and more. Sidhu said the goal is to make Daft as transformational to unstructured data infrastructure as SQL was to tabular datasets in the past. The company was founded in early 2022, nearly a year before ChatGPT was released, and before many people were aware of this data infrastructure gap. They launched the first open source version of Daft in 2022 and are gearing up to launch an enterprise product in the third quarter. 'The explosion of ChatGPT, what we saw is just a lot of other folks who are then building AI applications with different types of modalities,' Sidhu said. 'Then everyone started kind of like using things like images and documents and videos in their applications. And that's kind of where we saw usage just [increase] dramatically.' While the original idea behind building Daft stemmed from the autonomous vehicle space, there are numerous other industries that process multimodal data, including robotics, retail tech, and healthcare. The company now counts Amazon, CloudKitchens, and Together AI, among others, as customers. Eventual recently raised two rounds of funding within eight months. The first was a $7.5 million seed round led by CRV. More recently, the company raised a $20 million Series A round led by Felicis with participation from Microsoft's M12 and Citi. This latest round will go toward bulking up Eventual's open source offering as well as creating a commercial product that will allow its customers to build AI applications off of this processed data. Astasia Myers, a general partner at Felicis, told TechCrunch that she found Eventual through a market mapping exercise that involved looking for data infrastructure that would be able to support the growing number of multimodal AI models. Myers said that Eventual stood out for being a first mover in the space — which will likely get more crowded — and based on the fact that the founders had dealt with this data processing problem firsthand. She added that Eventual is also solving a growing problem. The multimodal AI industry is predicted to grow at a 35% compound annual growth rate between 2023 and 2028, according to management consulting firm MarketsandMarkets. 'Annual data generation is up 1,000x over the past 20 years and 90% of the world's data was generated in the past two years, and according to IDC, the vast majority of data is unstructured,' Myers said. 'Daft fits into this huge macro trend of generative AI being built around text, image, video, and voice. You need a multimodal-native data processing engine.'
Business Wire
5 days ago
- Business
- Business Wire
Strategy Announces General Availability of Strategy Mosaic™, the AI-Powered Universal Intelligence Layer
TYSONS, Va.--(BUSINESS WIRE)--Strategy (formerly MicroStrategy) today announced the general availability of Strategy Mosaic™, a groundbreaking AI-powered Universal Intelligence Layer designed to enable AI applications. As organizations modernize their data infrastructures, they often encounter challenges with siloed systems that lead to inconsistent metrics and governance gaps. This lack of clean, connected, and organized data is one of the greatest barriers to AI adoption. Strategy Mosaic addresses this issue by connecting disparate data sources across the enterprise, providing consistent and secure access to information that empowers both business users and AI applications. Sitting atop any database or data warehouse, Strategy Mosaic allows organizations to access diverse data sources. This unified layer supports AI, applications, and analytics use cases, enabling rapid development of data products without the need for custom data warehouses. Unlike traditional data catalogs and virtual data warehouses, Mosaic uses business definitions and user-friendly objects to represent data. "With Mosaic, we've broken through the biggest barriers to business innovation: data silos, conflicting metrics, and high data transformation costs," said Saurabh Abhyankar, Chief Product Officer at Strategy. "Our powerful semantic graph ensures a single source of truth for enterprise analytics, and Mosaic extends this with a universal layer of intelligence compatible with any cloud, reporting tool, and data source." Key features of Strategy Mosaic include: Rich Semantic Layer: Built on Strategy's proven semantic architecture, this ensures consistent business definitions and metrics across data sources. It includes comprehensive semantic concepts such as hierarchies, multi-form attributes, and transformations, all while centralizing security and governance policies. Universal Access: Mosaic features a standard access layer with support for third-party workloads via standard SQL (JDBC), DAX, REST, and Python APIs. It provides optimized connectors for popular tools like Tableau, Power BI, Excel, and Google Sheets, and can connect to over 200 data sources, including files, applications, and databases. AI-Powered Data Modeling (Mosaic Studio): Mosaic Studio uses AI to automate data preparation and modeling tasks, delivering results up to 10 times faster. It analyzes data, auto-creates objects and relationships, applies aggregation functions, detects and removes duplicates, and enriches underlying data. Users can even create metrics using natural language prompts. Query Acceleration Engine: This powerful in-memory engine enhances performance through push-down processing and cross-data source calculations, alleviating the load on data warehouses and reducing query costs. Enterprise-Grade Security & Governance: Strategy Mosaic ensures comprehensive security and governance with features such as security filters, object-level access control, granular user privileges, and flexible authentication methods, applied consistently across all access points. Sensitive data remains protected from exposure to underlying LLMs. Strategy Mosaic effectively addresses common pain points such as inconsistent metrics, security gaps, lack of AI readiness, and high analytic costs. Strategy Mosaic is available immediately. To learn more, visit About Strategy MicroStrategy Incorporated d/b/a Strategy (Nasdaq: MSTR/STRK/STRF/STRD) is a publicly traded company that has adopted bitcoin as our primary treasury reserve asset. By using proceeds from equity and debt financings, as well as cash flows from our operations, we strategically accumulate bitcoin and advocate for its role as digital capital. Our treasury strategy is designed to provide investors varying degrees of economic exposure to bitcoin by offering a range of securities, including equity and fixed-income instruments. In addition, we provide industry-leading AI-powered enterprise analytics software, advancing our vision of Intelligence Everywhere. We leverage our development capabilities to explore innovation in Bitcoin applications, integrating analytics expertise with our commitment to digital asset growth. We believe our combination of operational excellence, strategic bitcoin reserve, and focus on technological innovation positions us as a leader in both the digital asset and enterprise analytics sectors, offering a unique opportunity for long-term value creation. Strategy, Strategy Mosaic, and MicroStrategy are either trademarks or registered trademarks of MicroStrategy Incorporated in the United States and certain other countries. Other product and company names mentioned herein may be the trademarks of their respective owners. For more information about Strategy, visit
TechCrunch
5 days ago
- Business
- TechCrunch
How a data processing problem at Lyft became the basis for Eventual
When Eventual founders Sammy Sidhu and Jay Chia were working as software engineers at Lyft's autonomous vehicle program, they witnessed a brewing data infrastructure problem — and one that would only become larger with the rise of AI. Self-driving cars produce a ton of unstructured data from 3D scans and photos to text and audio. There wasn't a tool for Lyft engineers to that could understand and process all of those different types of data at the same time — and all in one place. This left engineers to piece together open source tools in a lengthy process with reliability issues. 'We had all these brilliant PhDs, brilliant folks across the industry, working on autonomous vehicles but they're spending like 80% of their time working on infrastructure rather than building their core application,' Sidhu, who is Eventual's CEO, told TechCrunch in a recent interview. 'And most of these problems that they were facing were around data infrastructure.' Sidhu and Chia helped build an internal multimodal data processing tool for Lyft. When Sidhu set out to apply to other jobs, he found interviewers kept asking him about potentially building the same data solution for their companies, and the idea behind Eventual was born. Eventual built a Python-native open source data processing engine, known as Daft, that is designed to work quickly across different modals from text to audio and video, and more. Sidhu said the goal is to make Daft as transformational to unstructured data infrastructure as SQL was to tabular datasets in the past. The company was founded in early 2022, nearly a year before ChatGPT was released, and before many people were aware of this data infrastructure gap. They launched the first open source version of Daft in 2022 and are gearing up to launch an enterprise product in the third quarter. 'The explosion of ChatGPT, what we saw is just a lot of other folks who are then building AI applications with different types of modalities,' Sidhu said. 'Then everyone started kind of like using things like images and documents and videos in their applications. And that's kind of where we saw, usage just increased dramatically.' Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | REGISTER NOW While the original idea behind building Daft stemmed from the autonomous vehicle space, there are numerous other industries that process multimodal data, including robotics, retail tech, and healthcare. The company now counts Amazon, CloudKitchens and Together AI, among others, as customers. Eventual recently raised two rounds of funding within eight months. The first was a $7.5 million seed round led by CRV. More recently, the company raised a $20 million Series A round led by Felicis with participation from Microsoft's M12 and Citi. This latest round will go toward bulking up Eventual's open source offering as well as creating a commercial product that will allow its customers to build AI applications off of this processed data. Astasia Myers, a general partner at Felicis, told TechCrunch that she found Eventual through a market mapping exercise that involved looking for data infrastructure that would be able to support the growing number of multimodal AI models. Myers said that Eventual stood out for being a first mover in the space — which will likely get more crowded — and based on the fact that the founders had dealt with this data processing problem firsthand. She added that Eventual is also solving a growing problem. The multimodal AI industry is predicted to grow at a 35% compound annual growth rate between 2023 and 2028, according to management consulting firm MarketsandMarkets. 'Annual data generation is up 1,000x over the past 20 years and 90% of the world's data was generated in the past two years, and according to IDC, the vast majority of data is unstructured,' Myers said. 'Daft fits into this huge macro trend of generative AI being built around text, image, video, and voice. You need a multimodal-native data processing engine.'
Yahoo
5 days ago
- Business
- Yahoo
Caylent Accelerate™ Modernizes Legacy Databases on AWS 3x Faster with Built-In AI Automation
AI-powered solution automates up to 70% of the migration process to help enterprises modernize faster—without the risk; breaks vendor lock-in and eliminates licensing costs IRVINE, Calif., June 24, 2025 /PRNewswire/ -- Caylent, an Amazon Web Services (AWS) Premier Tier Services Partner, today announced Caylent Accelerate™, an AI-powered solution that enables organizations to modernize their legacy databases up to three times faster than previous approaches. Designed to derisk and speed up complex database migrations by moving to open source databases on AWS, Caylent Accelerate helps customers eliminate licensing costs, lower total cost of ownership, reduce risk, and unlock modern cloud-native capabilities faster. Legacy databases—especially proprietary systems—are a costly barrier to business growth. These systems account for over $200 billion in annual licensing spend and contribute to more than $2 trillion in technical debt across U.S. enterprises. However, migrations to open source alternatives are notoriously slow, manual, and error-prone, often requiring niche expertise and months of effort—factors that lead many organizations to delay the modernization necessary to innovate and scale their business. Caylent Accelerate directly addresses these challenges with a highly automated, AI-first approach that delivers a 70% reduction in manual effort. Built on Amazon Bedrock and powered by large language models (LLMs), Caylent Accelerate translates complex SQL code, maps dependencies, generates test cases, and delivers migration-ready output with expert oversight. This combination of AI and human-in-the-loop validation ensures speed, accuracy, and business continuity. Bobby Land, chief product and technology officer at Teamfront, a strategic partner for founder-led software companies, and a Caylent customer, said, "Caylent Accelerate allowed us to seamlessly migrate from SQL Server to Aurora PostgreSQL with AWS instances—with minimal disruption and immediate access to modern, scalable infrastructure. Without it, manually migrating and testing each stored procedure would have required thousands of hours of effort. Caylent Accelerate™ cut through the complexity and dramatically reduced the migration effort, achieving a time savings for us of over 90%." Unlike "lift-and-shift tools" or other traditional approaches, Caylent Accelerate fast-tracks both the technical execution and strategic outcomes of modernization. It eliminates licensing costs, shortens project timelines, and enables enterprises to consolidate technology stacks for greater operational efficiency. With seamless integration into Amazon Aurora, Amazon RDS, and Amazon Redshift, Caylent Accelerate helps organizations unlock open, scalable, AI-ready infrastructure that supports long-term growth and innovation. "We designed this solution to automate the hardest parts of database migration—from translating stored procedures to validating new code," said Lori Williams, CEO at Caylent. "With Caylent Accelerate, we're collapsing the time it takes to eliminate technical debt and freeing our customers to focus on building what's next. This is the future of services: productized expertise, purposeful automation, and a relentless focus on outcomes that scale." Caylent offers a free database modernization analysis that provides organizations with a clear view of their current environment and an AI-powered estimate of migration timelines, object complexity, and effort required. For a free assessment and to learn more about Caylent Accelerate, visit: About CaylentAs an AWS Premier Tier Services Partner, Caylent is shaping the future where AI transforms industries responsibly and with excellence. We help companies build the solutions they need to succeed in today's market while enabling organizational evolution to thrive in a rapidly changing technology landscape. Our AI-enabled delivery methodology combined with our deep AWS experience turns our customers' ideas into impact, faster. Caylent's achievements include being named AWS Migration Consulting Partner of the Year, GenAI Industry Solution Partner of the Year, and Industry Partner of the Year - Financial Services in 2024, Application Modernization Partner of the Year in 2023, AWS Innovation Partner of the Year in 2022, and AWS Rising Star Partner of the Year in 2021. Caylent's services include migrations, modernization, custom software development and generative AI. Learn more at About TeamfrontFounded in 2023 and headquartered in Austin, TX, Teamfront is a strategic partner to the founder-owned software companies that are market leaders in niche verticals. Our team, comprised of seasoned executives in vertical SaaS, provides holistic operational support, playbooks, and best practices that enable our Team Cos to achieve their visions. Our commitment is to empower software companies to thrive and succeed in their unique domains. Together, we aim to thrive on this journey of growth. Learn more at View original content to download multimedia: SOURCE Caylent Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
