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Facebook F8 Takeaways – The Future is Private

F8, what was an 8- hour hackathon is now Facebook’s annual 2-day conference for developers, creators and entrepreneurs all around the world.

Conducted in McEnergy Convention Center in San Jose, CEO Mark Zuckerberg stressed his vision of building a privacy-focused social platform “as a product”  as he debuted the newest version of the company’s core app.

Digital Equivalent of a Living Room:

With the expansion of the digital world, Privacy fills the vacuum with a unique sense of purpose — giving us the power to be ourselves. F8 spent much time discussing privacy upgrades and improvements to social impact from the client side. The problem area of concern being security, algorithm fairness, privacy, misinformation, inclusion safety and care, accessibility, election integrity and content policy.

“For the last 15 years or so, we have focused on building Facebook and Instagram into the digital equivalent of town squares. But I believe that the future is private and over time, a private social platform will be even more important in our lives than digital town squares. So today, we’re going to start talking about what this could look like as a product”, said Zuckerberg which worked to set the tone for the rest of the conference. The core techs being implemented to resolve the problem area for every product team are computer vision, natural language processing, encryption, data framework, speech recognition, text-to-speech, liability tools, AI infrastructure, OCR and embedding.

Zuckerberg aims to change their business trajectory to win back the trust of the users by focusing their vision on 6 privacy principles for every one of their digital platforms.

  • Private Interactions
  • Encryption
  • Reduced Permanence
  • Safety
  • Interoperability
  • Secure data storage

“This isn’t just about building features,” Zuckerberg said. “We need to change a lot of ways we run this company.”

Privacy First Approach:

Facebook:
Initially designed as an alternative to the then social-media-champion, MySpace; Facebook’s design, flexibility and the key focus on amplifying social connections and distribution of public information, rocketed to become the social media sovereign within a span of 5 years.

In early 2018, plagued by public data breaches and scandals, the social media giant was under heavy scrutiny for its management of user data. Zuckerberg didn’t dodge the issue at F8.
“I know we don’t have the strongest reputation on privacy right now, but I’m committed to doing this well and starting a new chapter for our products.” He meant it as a joke that wasn’t.
Instead of what Facebook is, F8 was about what Facebook wants to be.

The first thing to have been rolled out in the conference is FB5 with its big redesign making it lighter, faster and cleaner.De-emphasising its news feed and prioritizing groups and events. “Friends” are  no longer the centre of the experience. With the launch focus has been made to build a community and make “communities as central as friends”.

Messenger:
The Facebook Messenger also got an overhaul for its upcoming LightSpeed with a rebuilt architecture making it 2x faster, 7x smaller, simpler, more reliable and more secure. With the last year messenger launch M4, it was the first step towards the vision.
“People’s communication styles are migrating toward messaging way faster than anyone thought,” said Stan Chudnovsky, head of Messenger. “And people want to communicate with businesses the same way.” With messages being end-to-end encrypted, the messenger is now the fastest and most secure messaging platform.
For business, an automated system has been created that allows customers to book an appointment through messenger.

The all-new desktop app has some new features for business users. It also allows its users to host group video calls and collaborate on projects. The AI smart camera is using the “pose detection” tech to give a hasslefree and even more life like experience.

Instagram:
Instagram updates basically focused on giving the users the ability to shop directly from the makers and “Support the people who make”, and raise funds within the app.
Instagram is also testing hiding the total number of likes a post receives to bring back the focus on connection than posting for likes.
Stories now don’t have to start with the camera anymore. Users can now get more creative with their stories. They can now raise money for charitable causes with a new donation sticker on their stories.

Finally, the Instagram camera will be updated with the “create mode” allowing to post effects and interactive stickers without having to take a photo or record a video.

Whatsapp:
Whatsapp updates deliver a private and intimate experience with end-to-end encryption. It now allows users to send their location privately with their friends and families. The company rolled out a product catalogue feature for small WhatsApp businesses and payment process that is being tested in India.

Zuckerberg left the audience with one final notion:
“This is about building the kind of future we want to live in. To build a world where we can be ourselves and live freely and know that our private moments are only going to be seen by the people they want, where we can come together around community and commerce, where we build in the tools that we need to keep us safe from the beginning and prevent harm and we then are able to focus on all the good people are able to do. Both in private and in public, both the living room and the town squares.”

How do you think Facebook’s new direction would affect the users?  We’re hoping to see some more updates?
Let us know by commenting.
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

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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:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, 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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