Category: Data

In this article, we will consider the use of AWS Data Pipeline and its main characteristics.

The principle of pipelined data processing

The data processing pipeline itself is a set of transformations that must be performed on the input data. It is complicated, for example, because information always arrives at the input of the conveyor in an unverified and unstructured form. And consumers want to see it in an easy-to-understand form.

This processing improves resource utilization for a given set of processes, each of which uses those resources in a predetermined manner. A good example of a pipeline organization is an assembly conveyor in production, when a product goes through all stages in sequence, right up to the finished product. The advantage of this method is that all products along their path use the same set of resources, and as soon as some resource is released by a given product, it can immediately be used by the next product, without waiting for the previous product to reach the end of the assembly line. If the conveyor carries similar but not identical products, then it is a sequential conveyor; if all products are the same, then it is a vector conveyor.

The device for processing commands of the simplest processor includes four stages:

• fetching commands from memory;
• decoding;
• determination of the address and selection of the operand;
• execution.

The purpose of AWS Data Pipeline

Today, Amazon Web Services has released a new service that works with other services, namely, it can help transfer data between:

• S3
• MySQL RDS / External MySQL servers
• DynamoDB

AWS Data Pipeline allows the user to copy, migrate data from SQL and DynamoDB tables to S3 and vice versa.

The staggered nature of image analysis tasks initially dictates the choice of a data pipeline architecture for the underlying data processing infrastructure. The data pipeline allows you to:

• Combining filters: Image filters from a set can be combined with one another, forming a processing chain, within which a sequence of operations is performed on the original images.
• Exploring the Effect of Changing Parameters: Once the filters are chained, it is easy to change the parameters of any filter in the chained and explore the effect of changing those parameters on the resulting image.
• Memory streaming: Large images can be processed by modifying only the image blocks at any given time. Thus, it becomes possible to process large images that cannot be accommodated in RAM in any way.

By default, Data Pipeline provides several templates:

• Export from DynamoDB to S3
• Export from S3 to DynamoDB
• Copy from S3 to RDS
• RDS to S3 Rip
• Analyzing files in S3
• Migrating from non-RDS MySQL to S3

The process is easily customizable using a graphical interface. You drag and drop elements, set parameters, and so on.

In this article, we will discuss the best alternatives for a Data Science internship.

Why do we use Data Science?

Today Data Science is one of the most promising and popular areas for a career change and additional education. Data Science is the science of how to work with big data, analyze it and find useful relationships that can then be used for a variety of tasks.

This is especially true for the corporate world and all companies regardless of their industries and the countries they work with. In the digital big data environment, companies are receiving incredible amounts of information about their users and customers. For example, data on user behavior, their social and cultural background, different preferences in food, clothing, entertainment, political views, as well as shopping history, and other personal information.

In Data Science, as in other areas, there are different areas and specialties. Someone deals with recommendation systems for Netflix, someone – computer vision for Google, and someone – work with text for online translators. Processes can be automated almost everywhere, so a data scientist can work for a wide variety of companies.

What a Data Scientist should be able to do?

The skillset for a Data Scientist will depend on what tasks they face. If we talk about the base, then he must have knowledge in IT, mathematics, statistics and have a good understanding of the essence of the company’s business:

Applied Mathematics and Data Analysis:

• Ability to conduct experiments;
• Statistics and Modeling: from linear models to advanced machine learning methods;
• Data preparation: cleaning, selection, and transformation of features.

Technologies:

• Skills in programming DS-models (most often Python or R), knowledge of libraries;
• Skills of working with distributed computing technologies (Spark, Hadoop, etc.);
• Skills of writing productive code.

Business:

• Ability to translate business hypotheses into a mathematical problem statement;
• Ability to anticipate how the model can be used in business processes and what value it can bring;
• Understanding which approaches, models, and methods are applicable in specific business cases.

Where to learn?

Fast.ai is the best and most comprehensive introduction to deep learning is given by the authors of Fast.ai – this resource is free, and there are absolutely no ads on it. The course includes an introduction to machine learning, the practicalities of deep learning, computational linear algebra, and an introduction to natural language processing with a focus on programming. All courses on this site are united by an applied approach, so I strongly advise you not to pass by.

Kaggle. Machine learning contests are a great opportunity to practice building models. There you have access to a variety of datasets designed for specific tasks. According to the standings, you can compare your progress with other participants. And the results will also show you in which topics you have gaps and what needs to be tightened up.

TOP Data Science internships 2021

The list of the top vacancies for Interns in Data Science 2021 includes the following:

• • Search Engineer
  • Content Analysis
  • Machine Learning Engineering
  • Data scientist (Computer vision)
  • Data Analytics Internship
  • Senior Manager – Business Analytics
  • Associate Data Scientist