Big data in 2024 has outgrown its buzzword status. It’s everywhere now. It has already started fueling smart cities, automating systems, and personalizing just about everything you can personalize. Hence, the hype to get into this high-paying field is more than ever before (and rightly so!) But here’s the problem: with so many big data tools available, figuring out the ones that actually matter is becoming difficult for big data developers. Developers need to stay adaptable and know what will give them the edge. In this blog, we will look into the top tools for big data developers to rise above the average developers in 2024!
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- Apache Hadoop
Even in a world teeming with newer, shinier tech, Apache Hadoop is a big data tool that developers still rely on in 2024. Why? At its core, Hadoop does two things exceptionally well: storing vast amounts of data across a cluster of machines (thanks to its Hadoop Distributed File System, or HDFS) and processing that data in parallel (via its MapReduce programming model). This combination makes it perfect for tackling enormous datasets that a single machine simply couldn’t handle. Plus, it’s open-source and cost-effective, especially for companies looking to scale without overspending on hardware.
- Apache Spark
Another top big data tool for developers is Apache Spark. This along with Hadoop are OG tools that are still holding their ground. Thanks to in-memory computing capabilities, Spark processes large amounts of data much faster than traditional tools. For developers, that means you can run complex large-scale data operations without the headache of waiting for hours (or days) for results. Spark’s multi-language support is an added advantage too. Whether you are writing in Python, Scala, Java, or R, Spark will fit smoothly into your workflow.
- Apache Kafka
Designed for high-throughput and low-latency data transmission, Apache Kafka is another big player in this field. Learning Kafka can benefit you as a big data developer in 2024. At its core, Kafka is a distributed messaging system for seamless communication and exchange of data between different applications. This becomes crucial for modern architectures where data needs to flow between multiple systems quickly and reliably. Another key feature is its durability. Data is stored in a fault-tolerant manner, ensuring no messages are lost even during system failure. This reliability becomes vital for businesses to maintain consistent data integrity across their operations.
- Apache Flink
Apache Flink is an open-source stream processing framework for building high-performance and always-available applications. Supporting both bounded and unbounded data streams, Flink is versatile – it excels in real-time stream processing but is also suitable for batch, graph, and iterative processing tasks. One of Flink’s standout features that makes this one of the best technologies in big data analytics is its speed. It’s designed to handle millions of events in real-time. Flink’s architecture is optimized for stateful computations, allowing it to maintain application state while processing massive data streams.
- Apache Hive
While you are learning/working with other tools in the Apache ecosystem, it is also nice to know Apache Hive. Thanks to its ability to simplify complex data processing tasks, it stands out as a big data management tool for developers in 2024. Hive essentially bridges the gap between SQL and big data. Developers familiar with SQL can use their existing skills to write queries in HiveQL (Hive’s query language) which is then converted into MapReduce or Spark jobs under the hood. Hive excels in batch processing of data stored in HDFS, Amazon S3, or Azure Data Lake. While it is not exactly designed for real-time analytics, it is perfect for times when you need to periodically process large data volumes (like ETL workflows, data warehousing, and reporting).
Now, don’t get overwhelmed by the large names, these are tools that can be learned easily through effective training and practice. If you are training your own teams in big data tools and are puzzled about where to start from, EducationNest is the best place to go. They offer expert-led training on big data analytics for corporate teams to help companies build the exact skills they need in their employees.
- Google Cloud BigQuery
This big data technology for developers is extremely useful for real-time decision-making. Designed for the cloud, BigQuery eliminates the need for complex infrastructure setup. One of the standout features is its serverless architecture. With this, developers can run SQL queries on large datasets without any worries. Performance is another significant advantage of BigQuery. It leverages Google’s powerful infrastructure to execute queries at lightning speed, even on terabytes or petabytes of data. Many businesses operating on the cloud prefer this tool to avoid the hassle of maintaining hardware.
- Microsoft Azure HDInsight
If you are working with the Azure ecosystem, this should be a top choice for you. One of the key reasons why this big data technology is a favorite among developers is that it supports a lot of open-source frameworks apart from fitting smoothly with the Azure ecosystem like SQL Database, and Data Lake. Whether you work with Apache Hadoop, Spark, or Kafka, HDInsight simplifies the process of deploying and scaling these in the cloud. Thus, it is a versatile tool allowing developers to build comprehensive data pipelines for better insights.
- Data Bricks
Built on Apache Spark, Databricks is one of the best tools for big data analytics. It is designed to simplify the development and deployment of big data applications. One of Databricks’ standout features is how it streamlines workflow by integrating data processing and ML in one place. Developers can easily build, train, and deploy ML models without jumping between different tools. This integration is a huge time-saver for teams working on large datasets and advanced analytics. It also integrates well with most of the major cloud providers like AWS, Microsoft Azure, and Google Cloud.
Conclusion
As a developer, being well-versed in a range of big data tools is not just a nice-to-have. It has become essential especially since people have started to understand this is a high-paying field and are trying to enter this field actively. So it is important to market yourself better to stay above the race. Even if you are just looking to enter this field, having knowledge of the best big data tools of 2024 is your ticket to staying competitive.
At EducationNest, we specialize in empowering teams with the right big data skills they need. Our comprehensive big data analytics training programs are designed to help your employees get practical, hands-on experience in the latest tools and technologies.
