With the action-packed latest keynote stream at the Steve Jobs theatre, Apple unveiled a smorgasbord of innovation. Starting from the top dog, the new series of iPhone range from the 10.2-inch retina display iPad, the Series 5 Apple Watch to Apple arcade and Apple TV+; CEO Tim Cook got the Apple devotees all worked up at the event.
Unleashing the new range of Apple iPhones:
Featured with an ultra-wide triple camera, a super-powerful bionic A13 chipset with focus on Machine learning across the chip; and 4K video capturing with image stabilization at 60fps; Tim Cook introduced the new iPhone and then handed off to Phil Schiller for the walkthrough.
Starting from a price range of $699 and being available in three variants i.e. iPhone 11, iPhone 11 Pro, iPhone 11 Pro Max, Apple’s new launch took the adherents by the big billow. Losing share to local rivals in China seems to be betting that a lower-price point on its iPhone series and improved marketing can help move the needle.
Now selfie is a word of the past. With 12 MP slo-mo selfie capture at 120 fps or 4K at 60fps Apple gave the Gen Z a new buzz word, “Slofie”. The sleek compact design with a super retina XDR display makes the phone splash-proof, water-resistant and even dust resistant.
As per critics, the iPhone 11 is somewhat an upgraded version of iPhone XR. How badly do you want to save $100, is the biggest question of the hour.
Apple Watch Series 5:
The redesigned, bigger and smarter upgrade with customisable watch faces of the Apple Watch marks the beginning of the end of the war on bezels. The Apple Watch Series 5 marked the return of ceramic and the introduction of titanium.
Health and fitness are always the major focus, with communications being the minor. Apple in its new launch of smartwatch, besides the always-on retina display and compass, added the feature of a complete fitness band.
Apple iPad:
Creating more hustle and no hassle for the consumers. Apple launches it’s 10.2- inch retina display iPad with A10 fusion power chip for gaming. Powered by an 8MP back camera and a face time HD cam the new Apple iPad, now has a battery life of 10 hours.
Accessorised with a full-size smart keyboard and an Apple pencil makes it suitable for many productivity tasks from school assignments to official presentations; making it a struggle-free option for every situation.
Apple Arcade and Apple TV+:
Unlike any other game subscription services, the Apple Arcade comes with more than 100 incredibly exciting new sets of games. Designed by the top innovative developers of the world. With unlimited access to all the games, Apple Arcade is priced at a monthly subscription of only $4.99.
Not much to offer for the mass Indian consumers at the moment; Apple TV+ would be an upcoming American ad-free subscription, video-on-demand web television service.
TV+ will have far less than the deep catalogues offered by Hulu and Netflix. Though priced at $4.99/month, it will be years before Apple TV+ helps the company’s bottom line.
Apple’s September event was coupled with some hardware tweaks and surprising price drops.
Let us know what grabbed your attention about this year? If you missed the other major events of the year read our snapshots of Facebook F8 2019 and Google i/O 2019.
To know us in person, drop a Hi at hello@mantralabsglobal.com
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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground
In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?
This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.
Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.
Why was Data Warehouse the Best Move on the Board?
Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.
The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.
The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.
The True Depth of Data Lakes
While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.
Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.
However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.
Is the Data Lakehouse the Best of Both Worlds?
This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.
Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.
As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:
Feature
Data Warehouse
Data Lake
Data Lakehouse
Data Type
Structured
Structured, Semi-Structured, Unstructured
Both
Schema Approach
Schema-on-Write
Schema-on-Read
Both
Query Performance
Optimized for BI
Slower; requires specialized tools
High performance for both BI and AI
Accessibility
Easy for analysts with SQL tools
Requires technical expertise
Accessible to both analysts and data scientists
Cost Efficiency
High
Low
Moderate
Scalability
Limited
High
High
Governance
Strong
Weak
Strong
Use Cases
BI, Compliance
AI/ML, Data Exploration
Real-Time Analytics, Unified Workloads
Best Fit For
Finance, Healthcare
Media, IoT, Research
Retail, E-commerce, Multi-Industry
Conclusion
The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.
Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.
At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.
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