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How to Build a Collaborative Data Science Environment?

Data science has outgrown its nascent phase and now incorporates many people, communities and models within it. Communication channels and information and knowledge sharing platforms that have become popular are blogs, papers, GitHub, data science meetings and workshops. However, these are often limited due to various constraints. At one time, someone may find them too focused on theory and lacking in completed code, thus failing to test themselves on real-life examples. At other times, data scientists may find availability of all the data, codes and detailed models but find that some of the libraries or the entire framework is incompatible with their versions. These issues can crop up in both intra-team and inter-team cooperation. 

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Need for Data Science Environment

Hence, to ensure that the experience across groups remains the same, data scientists must all use the same platform. Herein the question crops up: how to build a collaborative data science environment? This ensures higher accuracy and lower processing times. It can only take place if all the participants employ the same cloud resources to which they have access to in an organization. 

Cooperation is essential in big companies, especially where there are multiple teams and each team has many different members. Fortunately, cloud technologies have become affordable today, which allows building the requisite infrastructure that can then support a platform for experimentation, modeling and testing. 

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When you wonder how to build a collaborative data science environment, various tools can come to your aid. One of the more common tools is Databricks. On the other hand, consider a case where you need to do your job in an existing cloud where the rules governing customer’s data policy are stringent. The tools are non-standard and configurations customized. In such cases, you would need your data science platform prebuilt to utilize opportunities. 

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Factors to Consider

Some of the factors that need to be considered in such a case are the developed models that you can adjust and reuse for other forecasts if the development and training environment is the same. Also, input data, models, and results should be available to all team members if the data lake security is tightly controlled. Data scientists should use customized data science tools and data sources in one location for more efficient and accurate analysis.

Thus, one can imagine a data science environment as a platform to analyze data in many different ways by a variety of individuals. They can include data scientists, business analysts, developers and managers. The entire data lake and all the compute nodes that are arranged in the form of CPU or GPU clusters together make up the data science environment. Since the most updated and reliable data is present in the data lake, and the storage is connected, the members can exclude data import and export operations. Training, testing and reporting get synchronized. Furthermore, participants can copy the last model configuration and the model is based on various parameters, as required. Let us now look a bit more in detail regarding the design and deployment of the environment.

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Minimum Environment Architecture

We will now look at a primary distributed file storage environment. In this, you can use, for example, Apache Hadoop. Apache Hadoop is an open-source framework that allows parallel processing, and individuals can use it to store massive data sets across various computer clusters. It has a trademarked file system known as Hadoop Distributed File System (HDFS). This system is essential and takes care of data redundancy across various nodes and scalability. In addition to this, there is the Hadoop YARN, which is a framework. It is responsible for scheduling jobs to execute data processing tasks across the different nodes. The minimum expected nodes are three in number for this environment, and it creates the 3-Node Hadoop Cluster. 

Note that streaming can be built into the environment with the Kafka stream processing platform in the case of continuous data ingestion coming from various sources. Stream processing does not include any separately designated task. The only function it does is by changing to parquet format the original delimiter-separated values. The parquet format is more flexible when compared to Hive, as it does not require any predefined schema. Note that there are cases when the streamed values are entirely different from the standard expectations, either customized transformation takes place or the data gets stored in the original format in the HDFS. The reason for a detailed explanation of this stage can be found in the fact that it is a highly vital part of the process. Since there are no dedicated projects or prepared analysis that the data can account for, the pipeline must make it available in a way so that the data scientist can begin working on a set with no loss of information. All the data is available in the data lake and is connected in designed use cases. Data sources may differ and can take the forms of different log files or various kinds of services and system inputs, to name just two.

Once the data lake is ready, the clusters must be configured so that the data scientists can enjoy an environment with all the needed tools and varied opportunities. The toolset required are explained subsequently. Carrying on with the existing example environment, Apache Spark can be installed on all nodes. This is a cluster computing framework, and its driver runs within an application master process that is managed on the cluster by YARN. The builder of the environment must also ensure that Python is there on all the nodes and the versions are the same with all basic data science libraries available. As an option, the environment maker may also choose to install R on all the cluster nodes and Jupyter Notebook on at least two. TensorFlow goes on top of Spark. Analytics tools such as KNIME are also recommended on either one of the data nodes or the attached servers.

Finally, once the environment is ready, the data science environment should provide all the data scientists and their teams ready cooperative access for all available data.

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