Curiosity Cost to Company: Why Continuous Learning Pays Off

Building a Real-Time Data Pipeline from Kafka to Snowflake

How to get the Spotify Refresh Token


## Table of Contents
1. [So What is Confluent Kafka?](#1-so-what-is-confluent-kafka-)
2. [Architecture Overview](#2-architecture-overview)
3. [Setting Up the Kafka Data Ingestion](#3-setting-up-the-kafka-data-ingestion)
4. [Transforming Data with ksqlDB (KSQL Streams)](#4-transforming-data-with-ksqldb-ksql-streams)
5. [Streaming and Batch Loading Data into Snowflake](#5-streaming-and-batch-loading-data-into-snowflake)
6. [Conclusion](#conclusion)

## 1. So What is Confluent Kafka? 🤔

Building a real-time data pipeline with a self-hosted version of Kafka can seem a little scary, but platforms like Confluent make it super easy to setup a streaming pipeline within minutes. In this tutorial I will guide you through setting up a streaming pipeline where data flows from a source into Kafka, gets processed by ksqlDB (Kafka’s SQL engine), and it is then delivered into Snowflake in near real-time.

**NOTE:** This tutorial assumes that you have a basic understanding of Apache Kafka.

Confluent Kafka is an deployment distribution of Apache Kafka. Basically it provides a fully managed platform for real-time data streaming.

At its core, Apache Kafka is a distributed event streaming platform used to build real-time pipelines and applications.

Kafka is designed in a way that it can handle large volumes of data with high throughput and low latency, making it ideal for use cases like real-time analytics, log collection, and event-driven microservices.

But why do we need to use Kafka at all? 😬
The beauty of Kafka lies in it's scalability and fault tolerance. Data is replicated across brokers to prevent loss, and topics can be partitioned for parallel processing.

In short, Confluent Kafka is Kafka made easier: it keeps Kafka’s core strengths in handling real-time streams, while also getting a user-friendly, cloud-based experience for setup and monitoring.

Before we move further, let's understand some terms that I am going to be using frequently:

- **Connectors:** A library of pre-built source and sink connectors that make integration with external systems (S3, Snowflake, Postgres etc.) in a scalable, fault-tolerant way.

- **ksqlDB (Kafka SQL):** A SQL-like stream processing engine that allows us to create real-time transformations and aggregations on Kafka topics using simple SQL statements. Kafka's flavor of SQL.

- **Schema Registry:** A centralized registry for data schemas (Avro, JSON Schema, Protobuf) that ensures producers and consumers share a consistent data format and handle schema evolution safely. This prevents data compatibility issues as your schemas evolve over time.

## 2. Architecture Overview

Let’s start with a high-level view of the streaming pipeline we’re building. The data flows through several stages:

![Kafka to Snowflake: Streaming Data Pipeline](/static/images/blog/Confluent-pipeline.gif)

**Kafka to Snowflake: Streaming Data Pipeline**

In this pipeline, Confluent Kafka acts as the central data stream hub, and Snowflake is the destination data warehouse. Here’s what each component does:

- **Source Data:** The journey of our data starts here. It could be application logs, IoT sensor readings, database changes, etc. In this tutorial, I'll be using Confluent’s Datagen connector (which generates mock data).

- **Kafka Source Connector:** The Datagen source connector will publish streaming events to a Kafka topic for us. You could replace this with a connector for real systems (e.g., a JDBC connector for databases, CDC tools for capturing changes, etc.).

- **Kafka Topic (Raw Data):** A Kafka topic is like a feed or stream where messages on the same subject are stored. The raw events from the source are written to a Kafka topic. These topics are partitioned for scalability and can retain data for a configurable period. (We can select the number of partitions but the default is 6)

- **ksqlDB Stream (Real-Time Transformation):** ksqlDB will read from the raw Kafka topic, apply SQL-defined transformations (such as filtering unwanted events, aggregating data, etc.), and write the results to another Kafka topic. Essentially, ksqlDB allows us to create derived streams or tables in Kafka through SQL queries.

- **Kafka Topic (Transformed Data):** This is the Kafka topic that receives the processed data (output of our ksqlDB queries). It is the data we will finally send to Snowflake.

- **Snowflake Sink Connector:** A Kafka connector that continuously reads from the transformed Kafka topic and writes the data into Snowflake. It acts as a bridge between Kafka and Snowflake, batching the stream or using Snowflake’s streaming API to load data with low latency.

- **Snowflake Table:** The final table in Snowflake where the streaming data lands.

This architecture decouples data producers from consumers: Kafka is the buffer and distributor of data, ksqlDB is the real-time processing engine, and Snowflake is the storage and query engine.

## 3. Setting Up the Kafka Data Ingestion

To get data flowing into Kafka, we’ll use Confluent Cloud (Kafka as a service) and the fully-managed Datagen Source Connector.

**Create a Kafka Cluster**

Create a Kafka Cluster on Confluent Cloud (if you don’t have one).

Select the Cluster Type, Provider, Region and Uptime SLA based on your requirements.

You can choose to create the cluster on a private network but it makes life a lot more difficult when you decide to try out Cluster Linking.

![Confluent Cluster](/static/images/blog/Confluent-cluter.png)

**Create a Kafka Connector**

In the Confluent Cloud interface, add a Source Connector and choose “Sample Data”. This connector will continuously generate events based on a predefined schema or template. You’ll need to configure a few key parameters (detailed next).

![Sample Data](/static/images/blog/sample-data.png)

**Step-by-Step Connector Configuration:**

- **Data Generation Template:** Choose a sample data model (e.g., orders, pageviews, financial).
- **Connector Name:** Give it a descriptive name (e.g., orders-datagen-connector).
- **API Key:** Use an API key you generated previously or generate a new one.
- **Input Message Format:** Choose the format (JSON, Avro, Protobuf).
- **Output Record Format:** Select JSON_SR (requires Schema Registry).
- **Output Topic:** Specify the Kafka topic (e.g., orders_raw).
- **Enable Connector Auto-restart:** Recommended.

**Example Message:**

```json
{
  "ordertime": 1505461992786,
  "orderid": 27358,
  "itemid": "Item_7",
  "orderunits": 9.315348659866387,
  "address": {
    "city": "City_",
    "state": "State_24",
    "zipcode": 56096
  }
}
```

![Confluent Sample Topic](/static/images/blog/sample-topic.jpeg)

## 4. Transforming Data with ksqlDB (KSQL Streams)

Raw data is often not immediately useful without some transformation. ksqlDB (KSQL) allows us to create streaming transformations on Kafka data using SQL syntax.

**Create a ksqlDB Cluster**

Before creating your ksql streams you will have to create a ksqlDB cluster. The process is extremely straightforward. Just enter the name of your cluster and cluster size depending on your workload and your are good to go!

**Create a ksql Streams**

ksqlDB treats Kafka topics as tables or streams depending on use-case. In our scenario, we’ll create a stream for the raw topic. For example, if our raw topic is `sample_data_orders`, we can define a ksqlDB stream on top of it:

```sql
CREATE STREAM orders_stream (
    order_id VARCHAR,
    customer_id INT,
    state VARCHAR,
    total_amount DOUBLE,
    order_time VARCHAR
) WITH (
    kafka_topic = 'sample_data_orders',
    value_format = 'JSON_SR',
    partitions = 3
);
```

Here we specify the schema (field names and types) and that the data format is JSON_SR (JSON with Schema Registry). Note: Since the topic has an associated schema in Schema Registry, ksqlDB can also fetch the schema automatically; you could alternatively use `VALUE_FORMAT='JSON_SR'` without listing columns and let ksqlDB infer the schema. In any case, we now have `orders_stream` as a ksqlDB representation of the incoming data.

**Filtering Stream Data (Stateless Transformation)**

Suppose we only want to pass through high-value orders to Snowflake (because anything below $1000 is not worth searching for😂). We can create a derived stream with a filter condition:

```sql
CREATE STREAM high_value_orders AS
SELECT *
FROM orders_stream
WHERE total_amount > 1000;
```

This statement does two things:

- It creates a new Kafka topic (often with the same name as the stream, here `HIGH_VALUE_ORDERS`)
- Continuously writes to it only those events from `orders_stream` that meet the WHERE condition.

The process is stateless – each order event is evaluated independently. If the order’s `total_amount` is greater than 1000, it is passed along; otherwise, it is dropped. This is similar to a WHERE filter in SQL on a static table, except here it’s continuous. As Confluent’s example shows, you can filter a stream like ORDERS to create a subset stream (e.g., orders from New York) using a similar CREATE STREAM ... AS SELECT ... WHERE ... clause (Filtering a ksqlDB Stream into a New Stream). Under the hood, ksqlDB creates a new Kafka topic for `high_value_orders`, and any consumers (like our Snowflake connector later) can just read from that topic to get already-filtered data. In fact, if we list Kafka topics now, we’d see an entry for this new stream, since ksqlDB streams are backed by Kafka topics.

**Aggregating Stream Data (Stateful Transformation)**

KSQL can also perform aggregations, which are stateful operations (meaning they require maintaining some state from previous events). For instance, perhaps we want to keep a real-time count of orders per state (to see how many orders each region is placing). We can do:

```sql
CREATE TABLE orders_by_state AS
SELECT state, COUNT(*) AS order_count
FROM orders_stream
GROUP BY state
EMIT CHANGES;
```

This will create a table (backed by a Kafka topic, often a compacted topic since it’s an aggregate) that updates counts per state whenever new orders come in. The COUNT(*) aggregation is stateful because to compute a running count, ksqlDB must store and update the count for each state as new events stream in. Similarly, any aggregation, windowing (like a 5-minute rolling average), or joins between streams will be stateful.

**Stateless vs Stateful Transformations:**

- **Stateless operations** do not depend on any history – each event is processed independently. Examples: filtering (WHERE clauses), projecting/selecting certain fields, simple transformations (like converting units, or computing a value from a single event). These do not require storing any past data. You input a record, you output a record (or drop it) purely based on that record’s content.

- **Stateful operations** depend on the accumulated state from many events. Examples: aggregations (COUNT, SUM, AVG over time or grouped data), windowed operations, or joins (which need to match events from one stream with another). These require maintaining context – e.g., a counter, a sum, or a buffer of events to join. In our pipeline, the filter for high-value orders is stateless (each order is considered by itself), while counting orders by state is stateful (the count for a given state builds over time with each order).

> “In a nutshell, stateful operations are dependent on previous events of the stream, whereas stateless operations are not.”

For this tutorial, our goal is simply to filter and maybe do a light transformation. We’ll proceed with the `high_value_orders` stream as the data we want in Snowflake. You could further transform it using additional KSQL queries. The nice thing about ksqlDB is you can chain these transformations—each resulting stream/topic can be consumed by another query or by external consumers. And everything is real-time: as soon as an event meets the condition, it’s available in the output stream typically within milliseconds.

Before moving on, here’s a quick example of a ksqlDB transformation query and its result:

- **Input Stream (orders_stream):** contains all orders.
- **Transformation (stateless filter):**
```sql
CREATE STREAM high_value_orders AS
SELECT * FROM orders_stream
WHERE total_amount > 1000;
```
- **Output Stream:** a Kafka topic `HIGH_VALUE_ORDERS` that now only has orders where `total_amount > 1000`. This output is continuously updated as new orders come in.

## 5. Streaming and Batch Loading Data into Snowflake

Confluent provides a Snowflake Sink Connector that pulls data from Kafka and inserts it into Snowflake.

**Configuring the Snowflake Sink Connector**

To set up the Snowflake Sink Connector in Confluent Cloud:

- **Snowflake Connection Info:** Provide your Snowflake account URL, User Name, and Private Key. (Read about generating Private Key for a role in Snowflake here)
- **Target Database, Schema, and Table:** Specify the Snowflake `database.schema.table` where data should be ingested. The connector can auto-create the table based on the Kafka topic name.
- **Kafka Topics to Ingest:** List the Kafka topic(s) to pull data from. Ensure the topic exists.
- **Data Format and Converters:** Choose Avro, JSON Schema, Protobuf, or raw JSON as the input format. Since we use JSON_SR, Confluent’s JSON Schema Converter should be selected.
- **Snowflake Ingestion Mode:**
  - **Snowpipe (Batch, Default):** Buffers messages and writes them in micro-batches via Snowpipe, introducing minimal delay (few second to few minutes).
  - **Snowpipe Streaming (Continuous):** Uses Snowflake’s Streaming API for near real-time ingestion, avoiding batch overhead (Sub-second delay).
- **buffer.count.records:** Maximum records per batch (e.g., 10,000).
- **buffer.size.bytes:** Maximum batch size (e.g., 5 MB).
- **buffer.flush.time:** Time-based flush (e.g., 60 seconds).
- **Parallelism and Scaling:** Configure `tasks.max` to match the number of topic partitions for efficient parallel ingestion.

**Batch vs. Streaming Ingestion**

- **Snowpipe Batch** is efficient for large batches but incurs per-batch cost.
- **Snowpipe Streaming** is more cost-effective for high-frequency, continuous data.

**Deploying the Connector**

After configuring the connector, launch. Monitor its status in Confluent UI and verify that data is landing in Snowflake.

## Conclusion

This completes the Kafka-to-Snowflake data pipeline. In this tutorial, we set up a basic real-time pipeline, generated sample data, ingested it into Kafka, processed it in motion with ksqlDB, and delivered it to Snowflake for storage.

If you found this guide helpful, follow me for more deep dives into data engineering! Feel free to like, comment, or share your thoughts - I’d love to hear how you’re using Kafka in your projects. Happy Streaming 🚀

A comprehensive tutorial on setting up a real-time streaming pipeline with Confluent Kafka, ksqlDB, and Snowflake.


## Table of Contents
- [Curiosity Cost to Company](#curiosity-cost-to-company)
- [Why Curiosity Matters](#why-curiosity-matters)
- [The Personal ROI of Curiosity](#the-personal-roi-of-curiosity)
- [How to Cultivate Curiosity](#how-to-cultivate-curiosity)
- [Companies That Foster Curiosity](#companies-that-foster-curiosity)
- [Measuring Your Curiosity ROI](#measuring-your-curiosity-roi)
- [The Future of Work and Curiosity](#the-future-of-work-and-curiosity)
- [Conclusion](#conclusion)
- [References and Further Reading](#references-and-further-reading)

# Curiosity Cost to Company

In the tech industry, we often talk about CTC (Cost to Company) in terms of salary, benefits, and perks. But there's another CTC that's equally important: **Curiosity to Company**.

The modern workplace is evolving at an unprecedented pace. According to the [World Economic Forum's Future of Jobs Report](https://www.weforum.org/reports/the-future-of-jobs-report-2020/), 50% of all employees will need reskilling by 2025 as adoption of technology increases. In this rapidly changing landscape, the capacity and enthusiasm for continuous learning what I call the "Curiosity Cost to Company" may be the most valuable asset both individuals and organizations can cultivate.

## Why Curiosity Matters

Continuous learning and intellectual curiosity are becoming the most valuable traits in today's rapidly evolving tech landscape. Companies that foster a culture of curiosity tend to:

- **Innovate faster** - Research by [Harvard Business Review](https://hbr.org/2018/09/curiosity) shows that cultivating curiosity improves innovation and reduces group conflict. Organizations like Google famously implemented their "20% time" policy, allowing engineers to explore projects of personal interest, which led to innovations like Gmail and Google News.

- **Adapt to changes more effectively** - A [Deloitte study](https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2019/reskilling-upskilling-the-future-of-learning-and-development.html) found that organizations with strong learning cultures are 92% more likely to develop novel products and processes. When Amazon pivoted from online bookstore to e-commerce giant to cloud computing leader, it was their culture of curiosity and learning that enabled such dramatic evolution.

- **Retain talent longer** - According to [LinkedIn's Workplace Learning Report](https://learning.linkedin.com/resources/workplace-learning-report), 94% of employees say they would stay at a company longer if it invested in their learning and development. Companies like Microsoft have transformed their culture under Satya Nadella to embrace a "learn-it-all" rather than "know-it-all" mindset, significantly improving both innovation and retention.

- **Solve complex problems creatively** - Research published in [Nature](https://www.nature.com/articles/s41562-018-0522-y) demonstrates that diverse thinking approaches and knowledge domains lead to more innovative solutions. When SpaceX revolutionized the aerospace industry, it was by bringing together expertise from multiple fields and encouraging cross-disciplinary curiosity.

The importance of curiosity isn't just anecdotal. A landmark [2018 study in the Journal of Personality](https://onlinelibrary.wiley.com/doi/abs/10.1111/jopy.12406) followed 346 employees across a variety of industries and found that those with higher curiosity scores were more likely to suggest creative solutions to problems, share knowledge with colleagues, and receive positive evaluations from supervisors.

## The Personal ROI of Curiosity

When you invest in your own learning:

1. **Skills stay relevant** - Technology changes rapidly, and staying curious helps you adapt. Consider the evolution from on-premises infrastructure to cloud computing: engineers who proactively learned cloud technologies like AWS, which [grew by 30% in 2020 alone](https://www.canalys.com/newsroom/global-cloud-market-Q4-2020), positioned themselves for success while others faced obsolescence.

2. **Career opportunities expand** - Learning across domains opens new paths. [IBM's Institute for Business Value](https://www.ibm.com/thought-leadership/institute-business-value/report/closing-skills-gap) found that professionals with cross-functional knowledge command salary premiums of 5-25% over specialized peers. The rise of roles like DevOps engineer, which combines development and operations knowledge, exemplifies how cross-domain learning creates new career trajectories.

3. **Problem-solving improves** - Diverse knowledge creates unique solutions. Research in [Psychological Science](https://journals.sagepub.com/doi/abs/10.1177/0956797612464658) demonstrates that exposure to diverse ideas improves creativity and problem-solving ability by up to 40%. Apple's success comes partly from combining technology with liberal arts—as Steve Jobs noted, ["Technology alone is not enough."](https://www.npr.org/sections/alltechconsidered/2011/10/25/141690134/jobs-was-right-tablets-are-car-sized-devices-iphone-can-be-car)

4. **Work becomes more fulfilling** - Learning triggers dopamine responses that increase satisfaction. A [Harvard University study](https://news.harvard.edu/gazette/story/2019/09/joy-neuroscience-happiness-brain-science/) found that curiosity activates the brain's reward systems in ways similar to food and money—explaining why learning feels inherently satisfying.

5. **Resilience strengthens** - According to research from [University of Pennsylvania psychologist Martin Seligman](https://positivepsychology.com/learned-optimism/), learning builds psychological resilience. Professionals who continuously expand their skills report feeling more confident about facing industry disruptions and economic downturns.

My own career trajectory illustrates this ROI. When I invested time learning web accessibility standards outside my core responsibilities, it unexpectedly became a critical skill when my company faced compliance requirements. That curiosity-driven learning translated directly into career advancement and compensation growth.

## How to Cultivate Curiosity

Here are some practical ways to enhance your curiosity:

- **Dedicate time each week to learning something new** - The "5-hour rule" followed by Bill Gates, Elon Musk, and other successful leaders suggests dedicating at least 5 hours per week to deliberate learning. Try using the [Pomodoro Technique](https://francescocirillo.com/pages/pomodoro-technique) to set aside focused learning blocks.

- **Read outside your immediate field** - [Farnam Street's multidisciplinary approach](https://fs.blog/mental-models/) suggests reading across domains builds mental models that improve problem-solving. Consider platforms like [Blinkist](https://www.blinkist.com/) for summaries of key books outside your field, or newsletters like [Orbital Index](https://orbitalindex.com/) for space tech or [Benedict Evans](https://www.ben-evans.com/newsletter) for tech strategy.

- **Connect with people from different disciplines** - Research from [Northwestern University](https://insight.kellogg.northwestern.edu/article/better_decisions_through_diversity) shows diverse networks improve innovation and decision-making. Join communities like [Hack Club](https://hackclub.com/) (for programmers) or [Interdisciplinary Network for Group Research](https://www.ingroup.net/) to expand your intellectual circle.

- **Ask "why" and "how" questions more frequently** - The "Five Whys" technique, [developed by Toyota](https://www.toyota-global.com/company/toyota_traditions/quality/mar_apr_2006.html), helps uncover root causes and deeper understanding. Apply this approach in code reviews, technical discussions, and when learning new technologies.

- **Experiment with side projects** - [GitHub's State of the Octoverse report](https://octoverse.github.com/) shows that side projects drive both skills development and career advancement. Consider building projects at the intersection of your current expertise and areas you want to explore.

- **Practice "productive distraction"** - Stanford researcher [Emma Seppälä](https://www.emmaseppala.com/wp-content/uploads/2017/03/1-s2.0-S1053810016303014-main.pdf) found that allowing time for mind-wandering promotes creative insights. Try techniques like morning pages (three pages of stream-of-consciousness writing) from Julia Cameron's [The Artist's Way](https://juliacameronlive.com/books-by-julia/the-artists-way-a-spiritual-path-to-higher-creativity/).

- **Adopt the Feynman Technique** - Nobel Prize-winning physicist Richard Feynman's [learning method](https://fs.blog/feynman-technique/) involves explaining complex topics in simple terms to identify knowledge gaps. This approach forces you to understand subjects deeply rather than superficially.

## Companies That Foster Curiosity

Organizations that systematically cultivate curiosity gain significant competitive advantages:

- **Google's "20% time"** - This famous policy [spawned Gmail, Google Maps, and AdSense](https://abc.xyz/investor/founders-letters/2004/ipo-letter.html), demonstrating how structured curiosity drives innovation.

- **Microsoft's learning culture transformation** - Under Satya Nadella, Microsoft shifted from a "know-it-all" to a "learn-it-all" mindset, contributing to the company's [market cap growing by over $1 trillion](https://www.cnbc.com/2019/06/21/microsoft-stock-has-been-the-best-performer-since-satya-nadella-became-ceo.html).

- **Pixar's "Braintrust"** - This cross-functional feedback group deliberately brings together diverse perspectives to improve storytelling, as detailed in Ed Catmull's book [Creativity, Inc.](https://www.creativityincbook.com/)

- **Spotify's "Hack Weeks"** - Regular periods for employees to explore creative projects have launched features like [Spotify's Discover Weekly](https://engineering.atspotify.com/2020/10/hack-week-at-spotify/), which significantly improved user engagement.

Companies can quantify curiosity's impact: a [Merck KGaA study](https://www.emdgroup.com/en/news/curiosity-study-20-09-2016.html) with over 3,000 workers found that highly curious teams had 3x greater creativity, 7x greater innovation, and 53% better cooperation than less curious teams.

## Measuring Your Curiosity ROI

How do you know if your curiosity investments are paying off? Consider these metrics:

1. **Knowledge compound rate** - Track how many new skills you develop annually and how they connect to create unique value propositions.

2. **Career flexibility index** - Evaluate how many different roles you could reasonably apply for today compared to a year ago.

3. **Problem-solving versatility** - Note instances where knowledge from one domain helped solve problems in another.

4. **Learning efficiency** - Measure how quickly you can acquire and apply new skills compared to previous learning efforts.

5. **Network diversity score** - Assess the range of disciplines, industries, and backgrounds represented in your professional network.

Tools like [Notion](https://www.notion.so/) or [Roam Research](https://roamresearch.com/) can help track these metrics through personal knowledge management systems.

## The Future of Work and Curiosity

The value of curiosity will only increase as we move forward. The [World Economic Forum](https://www.weforum.org/reports/the-future-of-jobs-report-2020/) predicts that critical thinking and active learning will be among the most important skills through 2025 and beyond.

As artificial intelligence automates routine tasks, the most valuable human contributions will come from creative problem-solving and novel connections between domains precisely the skills that curiosity develops. LinkedIn's [Global Talent Trends report](https://business.linkedin.com/talent-solutions/global-talent-trends) indicates that 91% of companies see soft skills as vital, with learning agility among the most sought-after traits.

## Conclusion

In the calculation of your professional worth, don't underestimate the value of your curiosity. It might not show up on your payslip, but it compounds in value over time in ways that traditional compensation cannot.

The investment in your intellectual growth is the highest-ROI activity in your professional life. When you cultivate curiosity, you're not just developing skills you're developing the meta-skill that enables all other skill acquisition.

As management theorist Peter Drucker presciently observed, ["In a time of drastic change, it is the learners who inherit the future. The learned find themselves equipped to live in a world that no longer exists."](https://www.goodreads.com/quotes/8093539-in-a-time-of-drastic-change-it-is-the-learners)

Remember: The most valuable investment you can make is in your own intellectual growth.

## References and Further Reading

1. Berger, W. (2018). *The Book of Beautiful Questions: The Powerful Questions That Will Help You Decide, Create, Connect, and Lead*. Bloomsbury Publishing.

2. Duckworth, A. (2016). *Grit: The Power of Passion and Perseverance*. Scribner.

3. Dweck, C. S. (2006). *Mindset: The New Psychology of Success*. Random House.

4. Gino, F. (2018). The Business Case for Curiosity. *Harvard Business Review*. https://hbr.org/2018/09/curiosity

5. Kashdan, T. B., & Silvia, P. J. (2009). Curiosity and interest: The benefits of thriving on novelty and challenge. *Oxford Handbook of Positive Psychology*, 2, 367-374.

6. Leslie, I. (2014). *Curious: The Desire to Know and Why Your Future Depends on It*. Basic Books.

7. Pink, D. H. (2011). *Drive: The Surprising Truth About What Motivates Us*. Riverhead Books.

8. World Economic Forum. (2020). *The Future of Jobs Report 2020*. https://www.weforum.org/reports/the-future-of-jobs-report-2020/


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