The future of Apache Spark
Data platforms operational costs are not longer hidden for organizations.
In the world of big data analytics, Apache Spark has emerged as a market standard. With its capability to process large amounts of data at lightning speed, it has revolutionized the way businesses work with data. But with the changing market trends, the future of Apache Spark is not without its challenges. In this article, I will take a closer look at the current state of Apache Spark and explore its future in the context of the latest industry trends.
A brief overview
Apache Spark is an open-source distributed computing system that can process large volumes of data quickly. It was developed at the University of California, Berkeley’s AMPLab in 2009 as a successor to the Hadoop MapReduce computing model. Since its inception, Apache Spark has gained significant popularity among big data enthusiasts and developers worldwide, thanks to its efficient and faster processing abilities.
Apache Spark is built to handle various use cases in big data analytics, including data processing, machine learning, and graph processing. It provides an interface for programming with multiple languages such as Java, Scala, Python, and R. Apache Spark’s resilient distributed datasets (RDDs) provide fault-tolerant in-memory storage that makes it faster and more efficient than other big data processing frameworks.
Apache Spark has come a long way since its inception, and it has become the go-to tool for big data analytics in many organizations. Its popularity can be attributed to its flexibility and scalability, making it a perfect fit for handling various big data use cases. With Apache Spark, companies can build complex data pipelines, enabling them to derive insights from massive amounts of data in real-time.
What is happening in the market?
Companies are increasingly recognizing the costs associated with implementing Apache Spark jobs, including the financial impact of high cloud computation consumption and the need for technical teams in place to support the infrastructure. This renewed focus has led to the development of new strategies for optimizing resource usage.
It’s worth mentioning that some companies are cutting their data project budgets and reducing the size of their data teams, despite an increase in the number of business use cases that need to be addressed. The question then arises: how can these companies do more with less? One potential solution could be to reduce the effort required during data pipeline development. This could be achieved through the use of modern features and tools that simplify the process.
The market trends
One significant trend in the big data industry is the re-emergence of modern data warehouses such as Snowflake and BigQuery. These data warehouses offer fast data pipelines that can be created using just SQL queries or DBT pipelines, eliminating the need for external IT infrastructure deployment. With modern data warehouses, organizations can create a more streamlined data architecture, reducing complexity and cost while improving efficiency. Additionally, modern data warehouses facilitated scalability, allowing organizations to easily adjust their data pipelines to meet changing business needs. As such, they represent a significant shift in the way that organizations approach big data analytics, providing a more cost-effective and efficient alternative to traditional data processing methods.
On one hand, Snowflake Data Cloud has released several features that help to reduce effort and costs. One of these features is Snowpark, which provides a new way to run Python code using Snowflake’s computation resources. This means that local clusters are not required to execute data processing jobs. Snowpark is now fully integrated into the Snowflake UI, making it easier for users to take advantage of this powerful feature. Additionally, Snowflake’s engineering team is integrating Streamlit.io into the UI, providing a way to deliver data apps for business users. By leveraging Streamlit.io, users can quickly and easily create interactive applications that help to visualize and analyze data, making it easier to gain insights and drive business value.
On the other hand, the Google Cloud platform team have integrated a new data pipeline called Dataform (https://dataform.co/) that is similar to DBT and helps to transform and move data to the final stage. Additionally, GCP has been releasing new features related to machine learning and Spark jobs without the need to deploy external infrastructure. These new features and capabilities represent a significant advancement in the data processing capabilities of the Google Cloud platform, making it easier for organizations to analyze large amounts of data without incurring additional costs.
However, Apache Spark is not backing down, and it is actively developing new features that will make it more competitive and could be used for machine learning and scientific complex analysis in the future.
In conclusion, operational costs are no longer hidden from organizations, and they are now better prepared to undertake new data initiatives that fit within limited internal budgets and controls. As such, the future of big data analytics is one that is defined by cost efficiency, streamlined workflows, and powerful tools that make it easier than ever to derive insights from data.
Jhon Carrillo
Let’s connect on https://www.linkedin.com/in/jhoncarrillo/
