The year 2017 proved to be one of the best times to invest in Mobile app development. Enterprises have now realized that mobile apps are now a necessity. A Smartphone is a very best way to meet customer demands and increase sales. In the year 2017, the projected rise is 254 billion free downloaded apps, up from 57.33 billion in 2012, which proves mobile development is one of the most actively growing sectors in the industry. The key players are Google apps, social media, lifestyle and gaming apps. Large companies (retailers, banks, hospitality, etc.) are using mobile applications for branding, increasing customer engagement, direct marketing, etc., while small and midsize businesses are also following the mobile trend and creating their own apps.

Internet of Things (IoT) and Wearable Apps

The idea of a smart home, smart cities, industrial IoT, automotive industry, smart health, and smart retail is growing. From healthcare to education and smart homes to smart offices, IoT apps will gradually start becoming mainstream in 2017. Though it might take some more time for IoT to fully take off, it is here to stay. Apple watch apps are already booming and will continue growing. Google has also released Android Things to increase IoT adoption. As we see an increase in demand for connected things, apps will be required for smart devices other than smartphones, and developers will have to keep innovating to meet diverse needs. Analysts predict that IoT will grow from $157.05 billion in 2016 to $661.74 billion by 2021, at a Compound Annual Growth Rate (CAGR) of 33.3%. This directly impacts the mobile app trends as IoT devices are controlled by smartphones.

Accelerated Mobile Pages

Since Google introduced the AMP project, page loading on mobile devices is accelerated. With AMP, you now see the quick loading of web pages on your mobile. Besides this awesome function, Google has declared that it will provide an isolated search index for the mobile web. From an SEO point of view, this step transforms all mobile app development approaches.

Accelerated Mobile Pages will help web apps to load more quickly on all mobile devices, reducing bounce rate. This change will also help publishers increase visibility to their ads and create a boom in the number of visitors.

Mobile Payments

Customers engaged in online shopping through Mobile Applications have been using Internet banking or Credit/ Debit cards to make payments. But with the introduction of Apple Pay and Google Wallet, customers are gradually shifting to m-commerce. It will be a good way for companies to harvest data and provide in-depth analysis of clients. As well as being a more comfortable payment option for customers.

Augmented Reality/Virtual Reality Apps

In 2017 AR and VR will start to go beyond entertainment and gaming. We saw the growth trend driven by consumer and business demand for new technologies last year. Both AR and VR possess huge potential when it comes to transforming a diverse range of industry sectors. The market for enterprise Augmented Reality will primarily be driven by industries such as retail, healthcare, engineering and real estate. Virtual Reality technologies will be more focused on the game and events sphere.

On-Demand Apps

On-demand applications are now very popular. They make our lives easier and more convenient, and we can use them from anywhere.

Enterprise Apps and BYOD

More and more organizations embrace the “Bring Your Own Device” model, we are going to see an upsurge in demand for hybrid enterprise apps. 72% of organizations have already started investing in BYOD for employees in some way or the other. In 2017 more than half of the world’s employers will require BYOD. Hybrid apps are known to run in the app form but are essentially mobile websites. This helps reduce the cost and speed the mobile app development process.

Cloud-based Apps

Mobile App Developers are designing more cloud driven mobile apps. With increasing use of cloud technology, it has become much quicker and easier to get data without impacting on your internal phone memory. With Dropbox, Google Drive and various other cloud apps, more and more mobile apps will be cloud driven. Cloud-based mobile apps are especially attractive for enterprises as they would largely alleviate the data security issues inherent in BYOD (Bring Your Own Device).

Android Instant Apps

Native apps that function like websites can attract more people with their convenience. Android Instant Apps don’t require installation all borders are removed from app and website. You can have all the advantages of your app within your site, with only one click. Run Android apps as seamlessly as loading a web page and save space on your smartphone, no unwanted app downloading

Application Security

The security of a smartphone is more crucial than ever because of the amount of personal user data it contains.Apps with built-in security features can make a large difference in the app space. More developers will be adopting the use of this platform, providing better UX with more security features. With this new programming language, we’ll begin to see more secure apps in the market. Mobile App Developers will be working harder than ever to ensure that every app they design comes with a safety guarantee in 2018.

Machine learning, Artificial Intelligence, and Chatbots

Customised, conversational commerce and chatting with online shoppers are highly lucrative for any business. Apps are increasingly learning about their user’s preferences. Sounds intelligent, but it is way more than that. There is something about playing with their heartstrings. It is emotional quotients of applications being upgraded by sheer bot observation! All off you know such amazing AI apps: Prisma, Siri, Google Now. We are expecting much more in 2017 keeping the AI mobile application development trend intact.

Lazy loading

Another great trend that we should expect in the upcoming year is related to the lazy loading technology. The thing is that the images that load for too long prevent the user from opening the whole article. It takes time to load the page, and users leave not willing to spend too much time on the page waiting. As a result, the bounce rate grows, and the conversion rates drop. So, to prevent this, technology gurus have decided to implement the lazy loading which basically means that images only load when their turn on the page comes. Otherwise, they do not overload the page.

Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

By : Sampada

Published on Dec 19, 2024

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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