What Languages and Tools Do Big Data Experts Use? Insights from 31 IT Leaders

21CTO Guide: This article summarizes the languages and tools commonly used by big‑data experts, such as Python, Spark, and Kafka.

To understand the current and future state of big data, we interviewed 31 IT leaders from 28 organizations and asked, “What are the most popular languages, tools, and frameworks you use for data extraction, analysis, and reporting?” The following article records their responses, summarized below.

Python, Spark, Kafka

With the push of big data and AI/ML, Scala and Python, as well as Apache Spark, are becoming increasingly popular.

For OLAP data‑warehouse migration, Python is less used for machine‑learning development, but developers find writing Python ML models very convenient because of extensive library support.

Kafka is used for streaming extraction; R and Python are used for programming development, and Java remains common. SQL is not disappearing, but it is not the best friend of big data; however, its openness allows broader data access, and Gartner has helped SQL on Hadoop regain relevance.

We see many data‑warehouse technologies such as Hadoop, Spark, and Kafka, and strong interest in Redshift, Snowflake, and BigQuery.

The ML stack now includes powerful tools like TensorFlow, which lowers the learning curve.

Kubernetes is the third major platform, gathering many enthusiasts and expanding its user base.

Other widely used open‑source tools include Spark, R, and Python, which is why the platform integrates with these ecosystems.

In big‑data workflows, a new node can be added that runs Python, R, or Spark scripts; the node becomes part of the pipeline.

R once dominated, especially in data‑science modeling, but Python now leads due to its extensive tooling and libraries.

People are exploring Spark and Kafka. Spark processes massive disk volumes at high speed. Kafka serves as a messaging system to feed data into Spark. R is well‑suited for analyzing historical data, building models, and handling real‑time streams.

Common tools and frameworks also include in‑memory relational databases such as VoltDB, as well as Spark, Storm, Flink, Kafka, and various NoSQL databases.

We provide LINQ‑style APIs for CRUD operations that can be called from many languages—C#, Go, Java, JavaScript, Python, Ruby, PHP, Scala, and Swift. Designed for high performance and predictable low latency, the database focuses on programmatic data access rather than declarative SQL, so SQL is not currently supported.

When customers need to analyze their current workloads, we add SQL support to export data to backend warehouses and pools for analysis. For data extraction, tools like Kafka and Kinesis are increasingly used as default communication pipelines.

We view SQL as the primary protocol for companies of all sizes to use platform data. For cluster deployment management, Docker and Kubernetes adoption is rapidly growing. For data extraction, many users rely on Apache Kafka; we recently earned Kafka Connector certification through the Confluent partner program. To improve analytics, we often pair Apache Spark with Apache Ignite as an in‑memory data store.

Apache Kafka has become a standard for near‑real‑time extraction of massive data streams (especially sensor data) into analytics platforms. To achieve the highest analytical performance, in‑database machine learning and advanced analytics are becoming critical for large‑scale predictive analysis.

For visualization, a wide range of tools exist—from Tableau and Looker to Microsoft Power BI, IBM Cognos, and MicroStrategy—giving analysts unprecedented choices to create visual reports quickly and accurately.

We employ multiple data extraction and indexing tools, with Apache Kafka and NIFI being the most common.

We use Hadoop YARN with HBASE/HDFS for persistent storage, then leverage projects such as Apache Zeppelin, Spark/Spark Streaming, Storm, SciKit‑Learn, and Elasticsearch for processing, predictive modeling, analysis, and deep learning, alongside commercial tools like Talend, Pentaho, and Tableau.

TensorFlow, Tableau, PowerBI

1) We use Amazon Athena (Apache Presto) for log analysis.

2) We use Mode Analytics for data visualization and reporting.

3) We use TensorFlow to analyze traffic patterns.

From an ML perspective, frameworks such as TensorFlow, PyTorch, Keras, and Caffe have driven major innovations in building models for large‑scale data.

BI use cases aim to broaden the audience for data dashboards, with tools like Tableau, Power BI, MicroStrategy, TIBCO, and Qlik expanding reach.

As technical teams move away from MapReduce, we see Spark gaining traction. Java and Python are increasingly popular. Kafka is used for data extraction, while visualization tools such as Visual Arcadia Data, Tableau, Qlik, and Power BI generate reports.

Many projects employ multiple languages and analytics tools. SQL remains prevalent, alongside data‑science languages like Python and R, as well as classic languages such as Java and C#.

Other

The open‑source world is growing, with more people turning to streaming data driven by the demand for real‑time answers.

The choice of extraction mechanisms varies—rich text, document classifiers, SciByte, data ontologies, intelligent tagging tools, and deep‑dive research all enrich big data.

Customers search browsers for content or ways to build their own tools; SQL remains the lingua franca of big data, working atop Hadoop and other databases.

OData is not new; it continues to be used on both server and client sides, alongside GraphQL for dynamic queries.

Server‑side programming sees new technologies such as MongoDB for storage and Redis for caching. AWS S3 is valuable for using Elasticsearch and S3 as backend data stores, with clear technology and design patterns established.

Users of R and Python stick with familiar tools; big‑data systems provide many APIs to support diverse input and output methods, catering to both talent and developer tool demands.

Large enterprises push for standardized BI and data‑science tools across thousands of users, integrating data catalogs, security, and acceleration features into a unified open‑source layer.

The big‑data landscape will quickly expand across all development environments, including on‑premises and cloud. Languages, execution engines, and data formats evolve, but the core value of big data remains: enabling customers to bypass disparate tools and standards, using drag‑and‑drop or code‑free environments to build repeatable data pipelines at scale.

Compiled by: Lao Xia