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Inside of Mobile World Congress 2016- Future Technology Trend

The Mobile World Congress is the largest mobile trade fair, which wrapped up in Barcelona on Thursday. The conference recorded 2199 exhibitors and attendance of over 101000 people, which was covered by 3,600 members of the international press and media.

This year the conference was dominated by the arrival of advanced technology like wearable technology, robotics, advanced mobiles, virtual reality, smart machines, ultra-fast 5G networks, INVISIBLE CHARGERS, connected objects, HOLOGRAMS, LiFi evolution and development of other advanced gadgets. MWC was more focused on the future trends in technology and the business impact of mobility and other tech gadgets. Many announcements were made and many technologies were showcased which gave goosebumps.

Here are some of the launches and future technology announcements that were made in MWC 2016 which caught attention of the visitors, business-hubs and media:untitled-infographic

Wearable
Tying in with the internet of things is wearable tech, it is expected to be a big deal. Plenty of activity trackers, sleep monitors and other devices were showcased, which keep track of your health and wellbeing.

The focus was around the activity around the wearable pavilion, with everything from smart watches and glasses to smart fabrics demonstrated.

Michael O’Hara of the GSMA points out that we are embedding mobile in everything in our lives – which makes the show the perfect place to showcase the latest mobile developments.

This opens up new opportunities for vendors, app developers, and accessory makers. The smartphone will become the hub of a personal-area network consisting of wearable gadgets. These gadgets will communicate with mobile applications to deliver information in new ways and enable a wide range of products and services in areas such as sport, fitness, fashion, hobbies and healthcare. Thus, wearable devices connected with smartphones will influence the next generation of mobile application development strategies.

Virtual Reality
Virtual reality was featured heavily at the show. With the speech and support by Mark Zuckerberg and a showcase of VR sets on the stage of the Samsung Galaxy, it grabbed spotlight in conference.

The headsets for the Galaxy line of smartphones is partly powered by Oculus, which is owned by Facebook, and is a good gateway product to the more advanced Oculus Rift that goes on sale in the next couple of months.  The conference proved good chance to showcase the latest updates on VR, as the device had some tweaks when initially previewed.

Lots of other wearable headsets were also showcased which are designed to press your mobile phone into service as a screen, making it a more budget-friendly way to get into VR. HTC and Sony also unveiled its PlayStation VR. Google also announced their work on a new headset to work with smartphones.

The emphasis on smartphone VR is going to be the next big thing, given that most of the ingredients to turn your phone into a virtual reality wonderland are already there. Everything will change a thousand times before it ever settles. VR device will attach unnoticed to the frame of your glasses, which would be connected through mobile apps; maybe it’ll be powered entirely by a button on your shirt or your brain waves, which would be connected by Application. We’ll use VR for everything from simple games and movies to robotic surgery and wildly futuristic military applications, which would be operated by Applications. We’re building better apps for future to connect with VR sets.

Internet of Things
Mobile technology is a large part of making the internet of things a more welcome prospect for consumers. Connected devices would soon infiltrate everything from your home to your car, allowing them to communicate through more open platforms than before.

“Smartphones have become a sort of black hole integrating a huge array of sensors, but mobile is now exploding back out to our environments.

“Sensors and connectivity are expanding beyond smartphones, on our wrists, bodies, cars, TVs, washing machines, but also in invisible places in buildings and the world around us,” Forrester’s Thomas Husson wrote.

While there were lots of discussions and speeches about mobile simply being a subset and key to unlock IoT revolution.

The future of mobile app development isn’t simply about our mobile phones and tablets anymore. The Internet of Things will be even bigger in the near future, even though current efforts are being made to make IoT better. Smart objects will be a part of the Internet of Things and will communicate through an App on a smartphone or tablet. Smartphones and tablets will act as remote controls, displaying and analyzing information, interfacing with social networks to monitor “things” that can tweet or post, paying for subscription services, ordering replacement consumables and updating object firmware.

As devices start to get even more interconnected, the opportunity for software developers, to add value to these smart devices will become ever greater. Eventually, the competition between these devices will be mostly based on which has the best quality software. This is where the future of mobile app development becomes an ocean of opportunity for mobile app developers.

untitled-infographic(1)LiFi evolution
Speculation, Apple may deploy Lifi support in future iOS devices continues, and MWC saw pureLiFi launch its LiFi-X dongle, an access point that connects to any LED light to help create a LiFi network. Harald Haas, CEO of pureLiFi, said: “It’s exciting that so many of the tech giants are now engaging directly with LiFi through pureLiFi technologies… We have witnessed rumours that Apple is investigating ‘LiFi-Capabilities’ in their latest iOS 9.0,” he added, “We now have a rail-track technology for the lighting industry to develop exciting and new business models around light as a service (LaaS).”

With the advent of LIFI, the limitations associated with slow networks will be a thing of the past. Mobile App architectures will have to scale up with better server specifications and more optimized code on the front-end to ensure that they don’t become limitations in the performance factor of mobile apps.

Holograms Make An Appearance:
Which science fiction fan has not dreamed of being able to speak to someone far away by hologram? Several firms believe this will be possible when faster 5G mobile networks are running.

Among them is US start-up Leia Inc, named after the heroine of the “Star Wars” franchise, which presented a system that creates a 3D image that appears to float above the screen of a tablet.

SK Telecom’s stand featured a beam of green light which caused different images to appear inside it such as a dolphin, a heart or a gymnast’s movement.

The Hologram technology is in its nascent stage currently, but it has plenty of rooms to prosper in the future. It runs on a software that relies on ultrasonic waves. With the advent of mobile apps, the Hologram technology is going to make communication easier and intuitive.
In short, MWC represented all horizontal and vertical sectors of the mobile industry, which would be future of new-age technology.

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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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