
Tag: Java
Customers running atop Snowflake’s cloud data warehouse soon will find new functionality, including the ability to build ETL data pipelines in Python, as well as the ability to expose pre-built analytic routines as data services. Read more…
Data scientists, machine learning developers and data engineers are turning decisively to the Python programming language, according to a new study.
An annual usage analysis released this week by O’Reilly Media also found a decided shift towards cloud native design for software, IT infrastructure and DevOps. Read more…
Apache Spark is one of the most widely used tools in the big data space, and will continue to be a critical piece of the technology puzzle for data scientists and data engineers for the foreseeable future. Read more…
Nothing is quite so personal for programmers as what language they use. Why a data scientist, engineer, or application developer picks one over the other has as much to do with personal preference and their employers’ Read more…
Containers have emerged as legitimate platforms for running data science workloads. But are Docker and Kubernetes here for good, or is it just a passing data science fad? We looked to Anaconda’s SVP of Products and Marketing Mathew Lodge for insights on the matter. Read more…
How will data scientists work in the future? Based on today’s trends and a new survey by Forrester, it seems likely that much of the work that data scientists do will revolve around centralized platforms that help to organize not just the data and the tools, but data scientists themselves. Read more…
There’s good news if you’re for a job in data science in 2016 — the number of job openings in the field appears to be rising as companies look to leverage big data for competitive advantage. Read more…
When it comes to picking a language for writing big data applications, developers have an embarrassment of riches at their disposal. Python and R have proven popular among data scientists, while Java has been the go-to language for those developing apps on Hadoop. Read more…
Hadoop is hard. There’s just no way around that. Setting up and running a cluster is hard, and so is developing applications that make sense of, and create value from, big data. Read more…
Despite its proven ability to affordably process large amounts of data, Apache Hadoop and its MapReduce framework are being taken seriously only at a subset of U.S. supercomputing facilities and only by a subset of professionals within the HPC community. Read more…