DevOps

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WebConcepts Web Server Concepts and Examples

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Apache Spark

Apache Spark version 1.0 was released in May 2014 and has been the most preferred data processing engine ever since. This technology was first designed by researchers at the University of California in 2009 to speed up operations in Hadoop systems.

What is Apache Spark?

Apache Spark is a fast, in-memory data processing engine with elegant and impressive development APIs that enable data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

Apache Spark is a fast, in-memory data processing engine with elegant and impressive development APIs that enable data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

Apache Spark briefly lets you process data in parallel. We can put this as an example: You have a data set of 1,000,000 rows. Let's use this dataset for machine learning. Let there be a combination of "label" and "text" in the dataset. We will give a text and determine which tag it is similar to with a classification algorithm. Normally it will be difficult to calculate within 1,000,000 data. However, with the spark architecture, we calculate the data in sets of 1000 by dividing it by 1000, for example, and calculate it much faster with an calculation such as the average of the set of 1000.

Apache Spark Features:

    1- Quick
    2- Powerful cache
    3- Real-time
    4- Different language support

Apache Spark Architecture

Apache Spark has a well-defined layer architecture where all its components and layers are loosely coupled. This architecture is further integrated with various extensions and libraries.

RDD (Resillient Distributed Datasets): It is the component on Spark Cluster that allows us to make calculations about data.

DAG (Directed Acyclic Graph): DAG in Apache Spark is a set of Vertices and Edges, where vertices represent the RDDs and the edges represent the Operation to be applied on RDD. In Spark DAG, every edge directs from earlier to later in the sequence. On the calling of Action, the created DAG submits to DAG Scheduler which further splits the graph into the stages of the task.

What is Apache Spar?

Apache Spark is a fast, in-memory data processing engine with elegant and impressive development APIs that enable data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

What can be done with Spark:

Apache Spark is a fast, in-memory data processing engine with elegant and impressive development APIs that enable data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

    1- Data integration: ETL (Extract, Transform, Load)
    2- Machine learning and advanced analytics
    3- Streaming

Some algorithms supported by mlLib are listed below;

Apache Spark is a fast, in-memory data processing engine with elegant and impressive development APIs that enable data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

Classification Algorithms
    1- Logistic Regression
    2- Logistic Regression
    3- Random Forest
    4- Gradient Boosted Tree
    5- Multilayer Perceptron
    6- Linear Support Vector Machine
    7- Naive Bayes

2. Regression
    1- Linear Regression
    2- Decision Tree
    3- Random Forest
    4- Gradient Boosted Tree
    5- Survival Regression
    6- Isotonic Regression

3. Clustering
    1- K-Means Clustering
    2- Bisecting K-Means
    3- Gaussian Mixture Model
    4- Latent Dirichlet Allocation (LDA)

4. Collaborative Filtering
    1- Alternating Least Square (ALS)

5. Frequent Pattern Mining
    1- FP-Growth
    2- PrefixSpan

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What does Scikit-Learn mean?

Scikit-learn is an important library for the Python programming language, which is often used in machine learning projects. Scikit-learn focuses on machine learning tools that include mathematical, statistical and general purpose algorithms that underpin many machine learning technologies. As a free tool, Scikit-learn is extremely important in many different types of algorithm development for machine learning and related technologies..

David Cournapeau

Scikit-learn was originally developed by David Cournapeau as a Google write code project in 2007. Later, Matthieu Brucher joined the project and started using it outside of his thesis work.

Scikit-learn provides a set of supervised and unsupervised learning algorithms in Python through a consistent interface. It is licensed under a permissive, simplified BSD license and is distributed under many Linux distributions that encourage academic and commercial use.

The library is built on SciPy (Scientific Python), which must be installed before using Scikit-Learn.

Scikit-learn does not implement everything related to machine learning. For example, it does not have extensive support for:

    neural networks,
    self-organizing maps (Kohonen networks),
    learning associative rule,
    reinforcement learning, etc.

Others basic python libraries

Scikit-learn is built on top of NumPy, SciPy, and Matplotlib, so you need to understand at least the basics of these two libraries to implement it effectively.

The scikit-learn library was prepared using Python's OOP (Object-oriented programming) method. You can also view all classes and functions from the link at the bottom. Scikit-learn consists of three basic APIs:

    Estimator API: It is the basic interface of Scikit-learn. In short, classes that can learn data are called estimators. It provides this learning with the fit method. For example, it can be a classification or regression algorithm.

    Transformer API: Estimators that can make changes and filtering in the dataset are called Transformers. So transformers are also estimators.

    Predictor API: Some estimators can also predict model accuracy for Supervised / Unsupervised learning, these estimators are called Predictor. A predictor makes predictions on previously unlearned data with the predict() method. It also uses the score() method.

What is the difference between Estimator and Predictor?

By learning the estimator data (train data), it can estimate certain parameters (like the median parameter in the example above) with the fit method. Predictor, on the other hand, makes a value estimation on the same data (train data) that it has not seen before (test data).

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