Every application generates data, but what do those data mean? This is a question all data scientists are hired to answer. There is no doubt that this information is the most precious commodity for a business. But making sense of data, creating insights and turning them into decisions, is even more important. As the data keep growing in volume, the data analytics pipelines have to be scalable to adapt the rate of change.

As part of enterprise artificial intelligence (AI) initiatives, data engineering teams are using a wide range of data analytics techniques, ranging from streaming analytics to machine learning to deep learning. This diversity in techniques has led to a corresponding diversity in software platforms and tools. Most data engineering teams are using data ingestion frameworks, such as Kafka; a combination of machine learning tools, such as Hadoop, Splunk, SAS Analytics, Spark, Python and R; and open-source deep learning packages, such as TensorFlow, Caffe and PyTorch. In traditional data analytics pipelines, data flows into enterprise environments from various internal and external sources and gets pre-processed and cleansed. Enterprises commonly use a "staging area" to store intermediate representations of pre-processed data.

As part of enterprise artificial intelligence (AI) initiatives, data engineering teams are using a wide range of data analytics techniques, ranging from streaming analytics to machine learning to deep learning. This diversity in techniques has led to a corresponding diversity in software platforms and tools. Most data engineering teams are using data ingestion frameworks, such as Kafka; a combination of machine learning tools, such as Hadoop, Splunk, SAS Analytics, Spark, Python and R; and open-source deep learning packages, such as TensorFlow, Caffe and PyTorch. In traditional data analytics pipelines, data flows into enterprise environments from various internal and external sources and gets pre-processed and cleansed. Enterprises commonly use a "staging area" to store intermediate representations of pre-processed data.