A data-driven culture is making new headlines in this technically advanced digital world. Big data processing technologies emerge as a true game-changer in this scenario.
These are collections of tools. Further, this helps in data-driven decision-making for the best outcome in business. This is all about using real-time data for strategic management. Meanwhile, software development companies use data processing technologies to gain actionable insights to enhance agility and precision.
But how do these tools and technologies help in data-driven decision-making?
So, this blog aims to discuss the top 7 advanced and real-time big data processing technologies for enhanced decision-making.
So, let’s dive deep into it!
Top 7 Big Data Processing Technologies For Enhanced Decision-Making
1. NoSQL Database
In custom software development companies, NoSQL databases help manage scalable storage nodes. Further, these big data processing technologies enhance reliability and improve data management.
Moreover, this database uses relational database tables to store data.
How do They help in enhanced decision-making?
- NoSQL database enables storing data of various sizes and shapes.
- Further, they consider both structured and unstructured data types.
- Besides relational databases, this data processing technology offers scalability and flexibility. For example, MongoDB and Couchbase.
- Often, they improve decision-making based on enhancing performance by storing large sets of distributed data.
- Custom software development companies use these diverse data to improve decision-making for strategic moves.
- These big data processing technologies often consider managing data from various sources like social media, log files, or others.
- This strategy helps in developing valuable insights in a software development company.
2. Apache Spark
Among big data processing technologies, Apache Spark is an open framework. This distributed data processing framework ensures versatility and speed in software development.
Further, it helps process extensive workloads in software development companies. Rapid and in-memory data processing in the big data ecosystem offers a wide range of real-time data handling.
But how do they get involved in enhanced decision-making? Let’s find it.
- Besides other data processing technologies, Apache Spark offers higher performance and speed in real-time data processing.
- Further, this is excellent in machine learning, interactive processing, and streaming big data analytics.
- In-memory processing in custom software development companies needs limited time to read disks, resulting in better decision-making.
- Open-source community and resources help in quick decision-making in resolving troubleshooting issues.
- Moreover, these big data processing technologies simplify data processing by providing DataFrame APIs and SQL.
3. Apache Flink
Among other data processing technologies, Apache Flink improves precision and agility in data analytics. Like Spark, it’s an open-source and distributed big data processing technology. For a custom software development company, it helps balance batch and real-time processing. Further, Flink is a versatile data processing framework.
Moreover, Apache Flink has impressive speed and flexibility in processing big data. It offers higher efficiency based on the real-time stream processing.
The following are the ways this framework helps enhance decision-making.
- It offers faster and real-time stream processing for high throughput data, leading to improved decision-making.
- Further, this is an ideal choice for a software development company. Because Flink streamlines real-time and batch processing at the same time.
- Moreover, Flink offers exactly-once semantics for high consistency among all data processing technologies. This eliminates data loss.
- Decision-making is better with Flink’s real-time or event-driven data processing technologies.
- Open community support enhances the scope for insightful decision-making.
4. Kafka Streams
Kafka Streams, among other big data processing technologies, offer an open-source library. Further, these enable real-time processing of data, which is valuable for decision-making. With this framework, data processing and analysis is easy.
Kafka Streams in the big data ecosystem provides high-definition data pipelines. It offers a scalable and robust messaging system for high-performance pipeline development.
Let’s find out how this technology offers effective decision-making in business.
- Quick and instant decision-making is possible with its real-time processing and low-latency-based data stream.
- Further, these data processing technologies offer horizontal scalability to handle growing data volume.
- Kafka has inherent fault tolerance, which makes data processing work even after node failure.
- Moreover, software development companies adopt Kafka for easy deployment. This is because they need no additional or external components to work.
- Like Flink, Kafka has exactly-once semantics, eliminating data duplication and loss.
5. Apache Beam
Apache Beam offers model-agnostic-based high-precision data processing. Apart from other big data processing technologies, this framework processes data without worrying about their origin.
Further, it has a unified programming model. It tackles data from various sources and formats.
Following are the ways Apache Beam offers better decision-making to software development companies.
- This data processing technology supports various programming languages, making data processing simpler.
- Moreover, Apache Beam follows a unified model for stream and batch processing and streamlines a wide range of data processing.
- It often offers versatile tools or systems to make quick and valuable decisions.
- For instance, custom software development companies use this tool for large-scale data processing.
- These data processing technologies include a wide range of data due to the high data-ingestion features.
6. AI, machine learning
Software development companies adopt AI and ML to transform big data into intelligent responses. Businesses use these cutting-edge technologies to analyze market trends and predict future responses.
Let’s discover how custom software development companies use these technologies for enhanced decision-making.
- AI helps identify patterns and trends in the current market, resulting in informed decision-making.
- AI and ML further automate data processing and lower the analysis time. Additionally, this optimizes decision-making for better performance.
- These tools help software development companies design customized solutions for better user experiences.
- Data-driven decision-making is possible with ML algorithms and AI adoption.
- Moreover, software development companies can streamline their workloads based on informed decision-making through AI, ML-driven data processing.
7. Edge Computing
Software development companies use edge computing as one of the prominent data processing tools. Meanwhile, this helps locate data processing closer to the origin, resulting in lower latency.
Further, edge computing helps move data from centralized cloud locations to the network edges. Due to this reason, data processing and decision-making become faster.
The following are the reasons why edge computing helps in decision-making.
- These big data processing technologies are responsible for high speed in making informed decisions. Further, this becomes possible by moving analyzed data near to the origin.
- Moreover, secure data processing provides high-valued decisions for business growth.
- High efficiency in edge computing supports well-informed decision-making and uplifting software development.
- Moreover, it improves performance and user interaction by locally processing big data.
Conclusion
Advancements in big data processing technologies are reshaping how custom software development companies design and deploy applications. Further, different technologies enhance decision-making capabilities by increasing scalability and flexibility in data processing. Adopting these technologies often streamlines workflow and improves overall performance.
