Many new updates happened for Android developers lately after Google I/O. Initially there was no restriction on some features but now they have updated them with some restrictions.

We have covered new features and the old features as well with new restrictions.

Here are the old features with new restrictions:

• Background Execution Limits

Whenever an app runs in the background, it consumes some of the device’s limited resources, like RAM. This can result in an impaired user experience, especially if the user is using a resource-intensive app, such as playing a game or watching a video.
To lower the chance of these problems, Android O places limitations on what apps can do while users aren’t directly interacting with them. Apps are restricted in two ways:

Background Service Limitations: When an app’s service is running in the background might consume device resources which may lead to bad user experience, to avoid these type of issues Android system applies a number of limitations on background services, this does not apply to foreground services, which are more noticeable to the user.
Broadcast Limitations: Apps targeted Android O can not use their manifest to register for implicit broadcasts. They can still register for these broadcasts at runtime, and they can use the manifest to register for explicit broadcasts targeted specifically at their app.

Note: The restrictions are applied by default applied to apps which are targeting Android O and in terms of other applications users can enable these restrictions from the Settings screen even if the app has not targeted Android O.

• Android Background Location Limits

Considering battery usage and user experience , background apps which are using Android locations APIs to fetch the user’s location will receive location updates less frequently when the app is being used in a device running Android O, developers who are using Fused Location Provider (FLP), Geofencing, GNSS Measurements, Location Manager, Wi-Fi Manager will get affected by this change.

• Notifications

Notification Badges

Notification Badges are the new way of notifying users regarding the new notifications arrived for a particular app, this will display badges on app icons in supported launchers which show notifications associated with one or more notification channels in an app, which the user has not yet dismissed or acted on.
Notification Channels

Using Notification channels developers can group their application’s notifications by category so that the user can apply few characteristics basing on the notification category. When you target Android O, you must implement one or more notification channels to display notifications to your users. If you don’t target Android O, your apps behave the same as they do on Android 7.0 when running on Android O devices.

Google says that the following characteristics can be applied to notification channels and that when the user assigns one of these, it will be applied channel- wide and they are as follows

Importance
Sound
Lights
Vibration
Show on lock screen
Override do not disturb

Here are some new features:

• New in UI and Styling

There are bunch of new features of UI and Styling are introduced in Android O and are as follows

1. Fonts

Android introduced fonts in XML through which we can use custom fonts as resources, You can add your custom font file in res/font/ folder to bundle fonts as resources and can access as a normal resource file and Android Support Library 26 introduce support for APIs to request fonts from a provider application instead of bundling files into your project which helps in reducing your application size
To use these font features on devices running Android API version 14 and higher, a developer needs to use the Support Library 26.

2. Auto Sizing Textviews

By using Support Library 26 Beta developers can now instruct to their app’s Textview to automatically increase or decrease the size to fit perfectly within the boundaries of the Textview.

3. Adaptive Icons

Adaptive icons can display app’s launcher icons in a variety of shapes across different devices for instance in Google Nexus the launcher icon might be in circular and in some Samsung device it might be squircle. Google says that with Android O, each device can provide a mask for the icon, which the OS can use to render all icons with the same shape. This will likely be embraced by OEMs(Original Equipment Manufacturer) who would like to have some unique looking home screens.

4. Autofill Framework

This framework will help the user by pre-filling the user information and user can save time as Filling out forms is a time-consuming and error-prone task. Users can easily get frustrated with apps that require these type of tasks. The Autofill Framework improves the user experience by providing the following benefits:

Less time spent in filling fields Autofill saves users from re-typing information.
Minimize user input errors Typing is prone to errors, especially on mobile devices. Removing the necessity of typing information also removes the errors that come with it.

• Picture in Picture Mode

In Android 7.0, Android TV users can now watch a video in a pinned window in a corner of the screen when navigating within or between apps whereas it was not available to other devices whereas from Android O Picture in Picture is available to all the devices, not just the Android TV.

• Kotlin For Android

Java is the mostly used programming language for the development of Android, When you run a Java application, the app is compiled into a set of instructions called Bytecode and runs in a virtual machine. Many alternative Languages has been introduced to also run on the JVM through which the resulting app looks the same for the JVM
JetBrains, known for IntelliJ IDEA (Android Studio is based on IntelliJ), has introduced the Kotlin language.Kotlin is a statically-typed programming language that runs on the JVM. It can also be compiled to JavaScript source code.

Why Kotlin For Android?

Interoperability with Java
Intuitive and easy to read
Good Android Studio Support
Safe to avoid entire classes of errors such as null pointer exceptions.
Less to write compared to Java
Safe to avoid entire classes of errors such as null pointer exceptions.
Versatile for building server-side applications, Android apps or frontend code running in the browser.

Stay tuned for more new updates on Android.

Check out these articles to catch the latest trends in mobile apps:

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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