- October 5, 2018
- Posted by: admin
- Category: Uncategorized
Big Data Technologies- As we know, currently big data is in a constant phase of growth as well as evolution. By IDC estimation, global revenue from big data will reach $203 billion by the year 2020 and also it is predicted that there will be around 440,000 big data related job roles in the US alone even with only 300,000 skilled professionals to grab them.
However, from the recent scenario form the March 2018, we look at the marked differences in the big data space hence can imagine what will be exciting on the horizon for big data by the completion of the year 2018.
Now, professionals, students, freshers and entrepreneurs need to be updated with the emerging Big Data technologies for better growth in this decade. Hence, in this article, I am listing 7 emerging Big Data technologies and trends for 2018-2019 that will help us to be more successful with time.
Apache is a project model which got its name from combining the terms for big data processes batch and streaming. It’s a single model which we can use for both cases.
Simply put, Beam = Batch + strEAM.
We only required to design a data pipeline once, and further choose from multiple processing frameworks, under the Beam model. We can choose to make our own batch or stream because our data pipeline is portable as well as flexible. We have one more flexibility that every time we want to choose a different processing engine or when we need to process batch or streaming data, we don’t need to redesign it.
Hence, from much greater agility and flexibility, teams can benefit to reuse data pipelines, and also can select the right processing engine for the multiple use cases.
While it comes to Airflow, it turned into the ideal technology for automated, smart scheduling of Beam pipelines in order to optimize processes and organize projects.
Moreover, pipelines are configured via code rendering them dynamic, and metrics have visualized graphics for DAG and Task instances, along with other beneficial capabilities and features. In addition, Airflow has the ability to rerun a DAG instance, if and when there is a failure.
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Article Credit: Analytics India
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