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5 things you need to know from Stack Overflow Survey 2016- JavaScript Continues to Rule The Web.

To gain insight into the state of development, every year tens of thousands of developers are surveyed by Stack Overflow through their service. Stack Overflow serves as a stage where designers share their work and make inquiries identified with coding. Consistently, the site overviews a huge number of engineers to get a thought regarding the present situation of the tech world, making it a standout amongst the most exhaustive designer study ever directed.

Every year the statistics of inside industry are fascinating and this year for 2016’s study, 50,000 to 56033 developers responded from across the industry from 173 countries– and the findings are fascinating, as well as insightful. The company found that JavaScript is still overwhelmingly the most popular development language, with more than 55.4 percent of people saying they use the language. PHP fell 4 percent in the last year to 25 percent, which Stack Overflow attributed to the rise of Node and Angular, but Microsoft’s Visual Basic is the most “dreaded” language. It was also found that 46 percent of the developers have no degree in computer science or any related field and that more than 57 percent of them check in code at work more than once a day.
Survey statistics of January 2016 showed, more than 45 million opened Stack Overflow in their web programs to pose a question or answer something asked by a kindred designer. Most respondents recognize themselves as full-stack designers with a number as high as 28%, trailed by back-end web engineers with 12.2%.

Among overall participants, the most popular developer job title was “Full-Stack Web Developer” at 28 percent, followed by “back-end developer” at 12 percent and around 11.4% percent call themselves an understudy, trailed by 8.4% developers who are Android, iOS, Windows Phone, and multi-stage development developers. Interestingly the most common developer age is 25-29, with more than 28 percent of respondents fitting into the category, followed by 23 percent at 20-25.
Stack overflow engineer overview 2016 designer occupations: Stack Overflow 2016- 1

If we move to the area where we’ll discuss the most utilized advances, JavaScript keeps on decision the web. It’s still the most mainstream programming dialect for web advancement with 55.4% individuals saying that they code in JavaScript. This colossally famous programming dialect is trailed by SQL Server (49.1%), Java (36.3%), C# (30.9%), PHP (25.9%), and C++ (19.4%).Stack Overflow 2016- 4

In “Stack Overflow engineer review 2016 most prevalent technologies”, if we discuss the inclining advancements on Stack Overflow, React, Spark and Swift (taking business sector from Objective-C ‘quickly’) are administering the graphs, while Node.js and Angular JS are on the rise.stackoverflow- 2016- 6

This year we asked respondents if they are Engineers, Experts, Hackers or any of the other descriptors we’ve frequently seen in job listings, business cards, and Twitter bios.
95% of developers identify as either a Developer, Programmer, Engineer, Senior Developer or Full-Stack Developer. Embedded Application Developers are most likely to identify as Engineers. Graphics Programmers are most likely to identify as Programmers.
But Developer is the runaway choice in this survey.Stack Overflow 2016- 5

The average developer has about 6.5 years of IT or programming experience. This isn’t necessarily professional experience (the average student tells us they have 3.4 years of experience). Developers gain experience by building things, even if they’re doing it unpaid or part-time. We’ve found this experience distribution to closely match that of more than 230,000 developers who make their CVs available on Stack Overflow.

Worldwide, the median Front-End Web Developer has 3.5 years of experience. The median Full-Stack Developer has 8 years of experience. And the median Engineering Manager has 13 years of experience.

Stack Overflow 2016- 1

The other points that were highlighted in Survey were, 69% of all developers tell us they are at least partly self-taught. (13% of respondents across the globe tell us they are only self-taught.) 43% of developers have either a BA or BS in computer science or a related field. 2% of developers have a PhD.

Overall, about 73% of developers tell us they think diversity is at least somewhat important in the workplace. 41% of developers say diversity is very important. And developers who most often influence hiring decisions are more likely to believe in the value of diversity than other developer types.

The saddest statistic in the Survey document, which has been highlighted as a major issue at many silicon valley tech companies, is that more than 92 percent of the respondents were male, showing just how gendered the industry really is and how far we need to go.

The study provides a lot of other interesting data if you’re looking for insights into where to move next, or if you’re looking for an easy pay bump. Mantra Labs has been continuously keeping watch on latest trends in Tech companies, to know more about latest trends, connect to Mantra Labs.

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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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