Is Apache Spark still relevant in 2025?

Which is better, Kafka or Spark?

So, Kafka is the better option for ensuring reliable, low-latency, high-throughput messaging between different applications or services n the cloud. Meanwhile, Spark allows organizations to run heavy data analysis and machine learning workloads.

Is there anything better than Spark?

Other important factors to consider when researching alternatives to Spark include reliability and ease of use. The best overall Spark alternative is Microsoft 365. Other similar apps like Spark are Front, Hiver, Superhuman Mail, and Gmelius.

When not to use Apache Spark?

In this article, we will explore some of the scenarios when data engineers should not use Apache Spark for data processing.

1. Small Data Volumes. ...
2. Simple Data Processing Tasks. ...
3. Single Node Processing. ...
4. Cost-Sensitive Applications. ...
5. Limited Cluster Resources. ...
6. Integration with Other Tools. ...
7. Complex Data Structures. ...
8. Lack of Expertise.

Is data science dead in 10 years?

No, data science isn't dead in 10 years; it's evolving, with AI automating routine tasks, shifting the focus to higher-level strategy, complex problem-solving, ethical judgment, and applying insights, meaning the field is growing and changing, not disappearing, though entry-level roles focusing purely on basic analysis might shrink. The demand for skilled professionals who can work with AI, interpret results, build robust systems, and translate data into business value will remain crucial and even increase. 

Does Netflix use Apache Spark?

One of the key tools Netflix relies on is Apache Spark. Let's break down how Netflix uses Spark to power its platform and provide a seamless user experience.

Why use Databricks over Spark?

In summary, while open-source Spark provides a powerful engine for large-scale data processing, Databricks Spark offers additional features and optimizations that can make it easier to work with big data in banking use cases like transaction processing.

Does OpenAI use Apache Spark?

This integration makes it easy to use the Apache Spark distributed computing framework to process millions of prompts with the OpenAI service.

What are the disadvantages of Apache Spark?

What are the disadvantages of Apache Spark? It has no file management system of its own, no real-time processing support, has issues with small files, and has a lesser number of algorithms. These are the key disadvantages of Apache Spark.

Which coding language is in demand in 2025?

Python, JavaScript, and Java are currently the most demanded programming languages, with 45.7% of recruiters looking to hire Python developers, 41.5% searching for JavaScript specialists, and 39.5% seeking Java experts.

Why is Spark so difficult?

The main problem is that with such an approach, an engine cannot benefit from modern CPU optimizations like caching. Another problem is that chained calls create overhead by themselves; for example, in Apache Spark RDDs, they may generate many temporary objects in memory that can lead to increased garbage collection.

Is Spark better than Python?

Spark is known for its high performance and can process data much faster than traditional data processing frameworks. It achieves this by processing data in memory and leveraging the power of distributed computing. Python's performance is generally slower than lower-level languages such as C++ or Java.

Can you make $500,000 as a data engineer?

Yes, a data engineer can absolutely make $500,000 or more in total compensation, especially at top tech companies (FAANG), leading fintech firms, or cutting-edge AI companies, but it requires mastering core skills (SQL, Python, system design), specializing in high-demand areas like AI/ML or Snowflake/Databricks, developing strong leadership/soft skills, and working in high-cost tech hubs like the Bay Area or NYC. It's about demonstrating significant business impact, not just technical knowledge, to reach senior or staff-level roles. 

What is the 80 20 rule in data science?

The 80/20 rule in data science, derived from the Pareto Principle, typically means data scientists spend 80% of their time on data cleaning and preparation (wrangling) and only 20% on actual analysis and modeling, the more enjoyable part. It's a rule of thumb highlighting the challenge of messy data but also suggests focusing on the crucial 20% of core concepts for efficient learning or prioritizing high-impact tasks to achieve 80% of results in projects, with modern tools aiming to reverse this ratio.
 

Is data science oversaturated in 2025?

Is data science oversaturated in 2025? Though more people are entering the field, data science is not yet oversaturated… There is still demand for professionals… with specialised skills in AI, predictive analytics, & machine learning.

Does AWS use Apache Spark?

Amazon EMR is the best place to run Apache Spark. You can quickly and easily create managed Spark clusters from the AWS Management Console, AWS CLI, or the Amazon EMR API.

Which is better, Apache Spark or PySpark?

Spark provides high availability by replicating data across the nodes in the cluster. It also provides fault tolerance by ensuring that the failed tasks are re-executed on other nodes. PySpark is a reliable framework and provides robust error handling and debugging capabilities.

Which language is best for Spark?

Scala is the best language to use for Apache Spark due to its concise syntax, strong type system, and functional programming features, which allow for efficient and scalable distributed computing. However, Python is also a popular language for Spark due to its ease of use and extensive libraries.

What are the cons of Spark?

Resource intensive: Spark's in-memory processing requires significant resources, particularly RAM. This can make it costly to run, especially for large datasets, as scaling Spark workloads may require substantial hardware investments. Complex tuning: Spark requires extensive tuning to optimize performance.

Why is Spark lazy?

Lazy evaluation in Spark means that transformations (like map(), filter(), groupBy(), etc.) are not executed immediately when they are defined. Instead, Spark builds a logical execution plan, often called a Directed Acyclic Graph (DAG), and defers computation until an action (such as collect(), count()) is triggered.

What has replaced Spark?

Alternatives to Apache Spark

• dbt. dbt Labs. 219 Ratings. ...
• StarTree. StarTree. Free. ...
• Domo. Domo. 49 Ratings. ...
• Snowflake. Snowflake. $2 compute/month. ...
• AWS Glue. Amazon. ...
• Dask. Dask. ...
• Amazon EMR. Amazon. ...
• Google Cloud Dataflow. Google.