Latest news with #datawarehouse
Forbes
2 days ago
- Business
- Forbes
What Companies Should Know About Implementing A Data Lakehouse
Abhik Sengupta, Principal Solution Architect, Hakkoda. Traditional data warehouses—once the backbone of business intelligence and reporting—are increasingly misaligned with today's data demands. The surge in data volume, velocity and variety has exposed their architectural constraints: rigid schemas, high storage costs, poor handling of semi-structured data, and reliance on batch-oriented extract, transform and load (ETL) processes. In a Teradata/Vanson Bourne survey from 2018, 74% of decision-makers were already citing analytics complexity as a challenge with data warehouses, and 79% said users lacked access to all the data they needed. By 2021, DBTA reported that 88% of organizations struggled with data loading in these environments, and 42% still relied on manual cleanup and transformation. These limitations are particularly problematic in cloud-native environments where real-time analytics, AI workloads and globally distributed teams demand flexibility and speed. To overcome these challenges, many enterprises are adopting lakehouse architectures, which are intended to unify the governance and performance of data warehouses with the scalability and openness of data lakes. As a principal solution architect, I've led several large-scale lakehouse implementations across platforms like Snowflake, Coalesce and Sigma. In this article, I'll explain how lakehouses can address legacy bottlenecks and what organizations should consider when modernizing their data platforms with this approach. Why Companies Are Shifting To Lakehouse Architectures At its core, a lakehouse stores structured, semi-structured and unstructured data in low-cost object storage while layering on transactional features, schema enforcement and version control through a metadata management layer. This enables organizations to build both batch and streaming data pipelines, maintain high data quality and support time travel and auditability within the same platform. A major advantage of lakehouses is their interoperability. Multiple analytics and machine learning engines can access the same datasets simultaneously, eliminating the need for redundant copies or specialized infrastructure. This can improve collaboration across teams, speed up experimentation and simplify data governance. By unifying ingestion, processing, analytics and AI workloads, lakehouses can reduce operational complexity while increasing agility. They can also provide a composable foundation for building domain-driven data products, enabling real-time personalization. In fact, a study published earlier this year in Information Systems found that the lakehouse is "inexpensive, quick and adaptable" like a data lake, while combining the "structure and simplicity of a [data warehouse] with the broader use cases of a [data lake]." From personal experience, in one project I worked on, onboarding time dropped by 40% due to reusable pipeline templates and declarative schema handling. Importantly, built-in features like versioning and time travel enable data auditability, governance and lineage tracking using tools such as Great Expectations and CloudWatch. That said, it's important to consider which engines—such as Spark, Snowflake and Athena—are supported to enable flexible, future-ready analytics environments. This will be particularly important as companies work to adopt AI. Unlike traditional data warehouses, lakehouses support diverse, large-scale datasets—including unstructured formats—within one repository. Versioning and snapshotting enable repeatable, auditable ML workflows. Support for Spark and Flink can allow scalable model training directly on fresh data, essential for real-time personalization and AI governance. Technical Architecture: Building A Real-World Lakehouse Stack Implementing a lakehouse architecture is a multiphase transformation that spans the full data life cycle, from ingestion to governance. It's not a one-size-fits-all deployment, but a set of strategic choices that must align with organizational priorities, technical maturity and interoperability needs: 1. Ingestion: This is the foundation, where teams must assess the nature of their data sources, expected latency and format diversity. Successful implementations typically use schema-aware tools that preserve metadata and support both batch and streaming pipelines to ensure consistency downstream. 2. Processing And Transformation: In this stage, raw data is converted into analytics- and ML-ready formats. Most lakehouse platforms support schema evolution, versioning and time-travel-like capabilities, allowing teams to build reproducible pipelines and accommodate changing data structures without data loss. 3. Implementing The Storage Layer: This typically uses cloud-native object stores (like S3, ADLS or GCS), with an open format and a metadata layer to manage immutability, partitioning and optimization. The goal is scalable, low-cost storage that enables fast access and governance at scale. 4. Query And Analytics: Lakehouses often support multi-engine interoperability, allowing business intelligence tools, SQL engines and ML frameworks to access the same governed datasets. Companies must catalog integration data and metadata consistently to ensure reliable performance and trusted insights. 5. Orchestration: Layers must accommodate schema evolution, rollback and modular pipelines. Most teams implement CI/CD for data workflows, using orchestration tools like Airflow, Dbt or Step Functions to ensure reproducibility and resilience. 6. Governance And Observability: Both of these functions should span the entire stack. Versioned metadata, data contracts, lineage tracking and quality testing tools (e.g., Great Expectations, Soda or Monte Carlo) play a central role in building trust and compliance across domains. What It Takes To Prepare For The Lakehouse Success with a lakehouse depends on more than just tooling—it requires team readiness, clear processes and thoughtful design. Organizations must build capabilities in schema evolution, cross-engine interoperability and performance tuning to meet latency and cost goals. For compliance (e.g., GDPR, HIPAA, SOX), the architecture must support data lineage, time-based audits and immutability. This includes implementing version-controlled metadata, retention policies, role- and policy-based access controls, encryption (at rest and in transit) and detailed logging. Observability and data contracts are essential to detect quality issues before they become compliance risks. Operationally, automation is key. Tasks like compaction, metadata cleanup and performance optimization must be built into workflows. While platform integration is improving, gaps remain in business intelligence and orchestration tools, making testing and validation critical. Finally, readiness also depends on people. Invest in upskilling through structured training, reusable frameworks and real-world pilots. These accelerate adoption and reduce errors. By addressing these concerns, companies can build a scalable lakehouse foundation—ready to support governed, high-performing data products and AI at enterprise scale. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Globe and Mail
10-06-2025
- Business
- Globe and Mail
Can Snowflake's Gen2 Launch Drive Strong Product Revenue Growth?
Snowflake 's SNOW data warehouse platform has been gaining traction as enterprises increasingly rely on scalable, cloud native infrastructure to unify, analyze and share large volumes of data. Snowflake supports diverse workloads, ranging from business intelligence and data engineering to machine learning and AI. SNOW's compute engine, the Standard Warehouse, has been a critical driver for platform engagement and query performance across its data warehouse platform. Standard Warehouse supports high-throughput analytical workloads that power BI dashboards, batch jobs and AI applications across Snowflake's enterprise base. Snowflake recently launched Standard Warehouse – Generation 2 (Gen2), which delivers up to 2.1X faster analytics performance and 1.9X faster execution compared to the existing platforms. The upgrade introduces a new execution engine and next-gen hardware, reducing query latency without requiring changes to customer workloads. Snowflake's Gen2 upgrade reflects its continued investment in core performance infrastructure. SNOW's innovative portfolio is driving clientele. In the first quarter of fiscal 2026, Snowflake added 451 new customers, bringing the total to 11,578 (up 19% year over year). Snowflake maintained a net revenue retention rate of 124%, implying strong expansion within its existing base. Expanding customer base is driving product revenues, which hit $997 million in the first quarter of fiscal 2026, reflecting 26% year-over-year growth. The figure beat the Zacks Consensus Estimate by 6.71%. As demand grows for faster, cost-effective data processing across enterprise and AI workloads, Snowflake expects Gen2 to contribute significantly to its consumption-based revenue growth. Snowflake Faces Stiff Competition Snowflake's data warehouse platform faces intense competition from Amazon AMZN and Microsoft MSFT, both of which are strengthening their cloud data infrastructure and analytics capabilities. Amazon is advancing its position through Amazon Redshift and Redshift Serverless, which allow enterprises to scale analytics workloads without managing clusters. Redshift integrates with AWS's AI services like SageMaker and Bedrock, helping customers run ML models and generative AI applications directly within the data warehouse. Amazon's serverless innovation aligns with growing enterprise demand for elastic and AI-ready infrastructure. Microsoft is reinforcing its presence in enterprise data with Microsoft Fabric, a unified analytics platform that combines Synapse, Power BI, and Data Factory with AI integrations. Fabric supports real-time analytics and large language model workloads, enabling seamless data collaboration across business teams. Microsoft's tightly integrated stack and expanding AI capabilities are positioning it as a formidable player in next-gen data infrastructure. SNOW's Share Price Performance, Valuation and Estimates Snowflake shares have appreciated 36.5% year to date, outperforming the broader Zacks Computer & Technology sector's return of 1.7% and the Zacks Internet Software industry's increase of 13.2%. SNOW's YTD Price Performance Image Source: Zacks Investment Research Snowflake stock is trading at a premium, with a forward 12-month Price/Sales of 14.48X compared with the industry's 5.69X. SNOW has a Value Score of F. SNOW's Valuation Image Source: Zacks Investment Research The Zacks Consensus Estimate for second-quarter fiscal 2026 earnings is pegged at 26 cents per share, unchanged over the past 30 days, indicating 44.44% year-over-year growth. Snowflake Inc. Price and Consensus The consensus mark for SNOW's fiscal 2026 earnings is pegged at $1.06 per share, which has declined by 8.5% over the past 30 days. The figure indicates a 27.71% increase year over year. Snowflake currently carries a Zacks Rank #3 (Hold). You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. Only $1 to See All Zacks' Buys and Sells We're not kidding. Several years ago, we shocked our members by offering them 30-day access to all our picks for the total sum of only $1. No obligation to spend another cent. Thousands have taken advantage of this opportunity. Thousands did not - they thought there must be a catch. Yes, we do have a reason. We want you to get acquainted with our portfolio services like Surprise Trader, Stocks Under $10, Technology Innovators, and more, that closed 256 positions with double- and triple-digit gains in 2024 alone. See Stocks Now >> Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report Inc. (AMZN): Free Stock Analysis Report Microsoft Corporation (MSFT): Free Stock Analysis Report Snowflake Inc. (SNOW): Free Stock Analysis Report
Yahoo
26-05-2025
- Business
- Yahoo
3 high-flying stocks with promising prospects
Expensive stocks often command premium valuations because the market thinks their business models are exceptional. However, the downside is that high expectations are already baked into their prices, leaving little room for error if they stumble even slightly. Separating true intrinsic value from speculation isn't easy, especially during bull markets. That's where StockStory comes in - to help you find high-quality companies that will stand the test of time. Keeping that in mind, here are three high-flying stocks expanding their competitive advantages. Forward P/S Ratio: 13.9x Founded in 2013 by three French engineers who spent decades working for Oracle, Snowflake (NYSE:SNOW) provides a data warehouse-as-a-service in the cloud that allows companies to store large amounts of data and analyze it in real time. Why Are We Fans of SNOW? Winning new contracts that can potentially increase in value as its billings growth has averaged 26.5% over the last year Platform plays a pivotal role in customer workflows as its net revenue retention rate punches in at 126% Expected revenue growth of 24.2% for the next year suggests its market share will rise At $199.63 per share, Snowflake trades at 13.9x forward price-to-sales. Is now the time to initiate a position? Find out in our full research report, it's free. Forward EV/EBITDA Ratio: 28.7x With a mission to democratize finance, Robinhood (NASDAQ:HOOD) is an online consumer finance platform known for its commission-free stock and crypto trading. Why Are We Backing HOOD? Customer spending is rising as the company has focused on monetization over the last two years, leading to 43.1% annual growth in its average revenue per user Additional sales over the last three years increased its profitability as the 64.6% annual growth in its earnings per share outpaced its revenue Free cash flow margin grew by 1,103.9 percentage points over the last few years, giving the company more chips to play with Robinhood is trading at $62.76 per share, or 28.7x forward EV/EBITDA. Is now the right time to buy? See for yourself in our comprehensive research report, it's free. Forward P/E Ratio: 136.9x Starting from a single Washington, D.C. location, CAVA (NYSE:CAVA) operates a fast-casual restaurant chain offering customizable Mediterranean-inspired dishes. Why Is CAVA on Our Radar? Aggressive strategy of rolling out new restaurants to gobble up whitespace is prudent given its same-store sales growth Customers are lining up to eat at its restaurants as the company's same-store sales growth averaged 13.8% over the past two years Free cash flow margin increased by 7.9 percentage points over the last year, giving the company more capital to invest or return to shareholders CAVA's stock price of $83.43 implies a valuation ratio of 136.9x forward P/E. Is now the time to initiate a position? Find out in our full research report, it's free. Donald Trump's victory in the 2024 U.S. Presidential Election sent major indices to all-time highs, but stocks have retraced as investors debate the health of the economy and the potential impact of tariffs. While this leaves much uncertainty around 2025, a few companies are poised for long-term gains regardless of the political or macroeconomic climate, like our Top 5 Growth Stocks for this month. This is a curated list of our High Quality stocks that have generated a market-beating return of 176% over the last five years. Stocks that made our list in 2020 include now familiar names such as Nvidia (+1,545% between March 2020 and March 2025) as well as under-the-radar businesses like the once-small-cap company Exlservice (+354% five-year return). Find your next big winner with StockStory today for free. 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
