Big Data Technology Stack in 2018 and Beyond: Trends and Predictions

Big data has been a driving force in the tech industry for over a decade, and its evolution continues unabated. This article delves into the big data technology trends that emerged in 2018 and the predictions for the future. As we step into 2023, these insights remain relevant and provide a comprehensive overview of the current landscape.

The Evolving Big Data Technology Stack

Big data technology is not a static field. It evolves with time, driven by advancements in AI, cloud computing, and streaming data technologies. In 2018, several key technologies and trends emerged, reshaping the big data stack:

TensorFlow and Big Data Integration

The integration of TensorFlow with big data platforms has enabled more sophisticated analytics. TensorFlow, an open-source machine learning framework, has become increasingly popular with developers.

Growing AWS Ecosystem

The AWS ecosystem continues to expand, offering a wide range of services for big data processing and analytics. AWS integrates seamlessly with other AWS services, making it easier for developers to build scalable and reliable data processing pipelines.

Cloud-Based Data Warehousing Solutions

Many companies are shifting their focus from on-premise data warehouses to cloud-based solutions. Snowflake Data Warehouse, for instance, has gained momentum, providing a cloud-native data warehousing solution that is highly scalable and user-friendly.

Kubernetes, Istio, and Knative for Big Data Infrastructure

Cloud-native technologies have become essential for managing big data infrastructure. Kubernetes, Istio, and Knative from Google have been instrumental in ensuring that both data processing and the underlying infrastructure can fade into the background, allowing developers to focus on building applications without worrying about the underlying infrastructure.

The Big Data Ecosystem in 2018

Hadoop: The Big Data Framework

Hadoop, a widely used open-source framework, plays a crucial role in big data ecosystems. It consists of a data layer (Hadoop Distributed File System, HDFS) and a data processing layer (MapReduce). HDFS is designed to store large datasets, while MapReduce enables distributed computing over these datasets.

Cloudera and Hortonworks Merger

The merger of Cloudera and Hortonworks at the beginning of 2018 marked a significant event in the big data industry. This merger unified two leading players in the Hadoop ecosystem, creating a more robust and comprehensive solution for big data processing and analytics.

FUTURE PREDICTIONS

Looking ahead, the big data landscape will continue to evolve, driven by several key trends and predictions:

Digital Transformation

Digital transformation will increasingly be discussed at the CXO level, highlighting the strategic value of data in driving business decisions.

Operationalization of Big Data Analytics

2019 promises to be a significant year for operationalizing big data analytics, making advanced analytics more accessible to a wider range of organizations.

Hybrid Data Analytics Deployment

More organizations will embrace hybrid data analytics deployment, combining on-premise solutions with cloud-based services to meet their specific data processing needs.

Machine Learning in Mainstream Use Cases

Machine learning will start appearing in more mainstream use cases, enabling businesses to leverage AI for a broader set of applications.

Bigger Innovation with Fewer Unicorns

As the big data market matures, we can expect fewer unicorns but a greater emphasis on innovation, enabling companies to stay competitive in the rapidly evolving landscape.

For detailed analysis of each prediction, please refer to the article Big Data 2019: 5 Predictions On Trends, Tech and How the Landscape Will Change.