Category Archives: data engineering
Data Ingestion Types – Concepts & Examples
Last updated: 17th Nov, 2023 Data ingestion is the process of moving data from its original storage location to a data warehouse or other database for analysis. Data engineers are responsible for designing and managing data ingestion pipelines. Data can be ingested in different modes such as real-time, batch mode, etc. In this blog, we will learn the concepts about different types of data ingestion with the help of examples. What is Data Ingestion? Data ingestion is the foundational process of importing, transferring, loading, and processing data from various sources into a storage medium where it can be accessed, used, and analyzed by an organization. It’s akin to the first …
Open Source Web Scraping Tools List
If you’re looking for a cost-effective way to access the data that matters most to your business, then web scraping is the answer. Web scraping is the process of extracting data from websites and can be used to gather valuable insights about market trends, customer behavior, competitor analysis, etc. To make this process easier, there are plenty of open source web scraping tools available. Let’s take a look at some of these tools and how they can help you collect and analyze data with greater efficiency. Beautiful Soup Beautiful Soup is a Python library designed for quick turnaround projects like screen-scraping. This library allows you to parse HTML and XML …
Data Analyst, Data Scientist or Data Engineer: What to Become?
There is a lot of confusion surrounding the job designations or titles such as “data analyst,” “data scientist,” and “data engineer“. What do these job titles mean, and what are the differences between them? Before selecting one of these career path, it will be good to get a good understanding about these job titles or designations, related roles & responsibilities and career potential. In this blog post, we will describe each title / designation and discuss the key distinctions between them. By the end of this post, you will have a better understanding of which career path and related designations are right for you! Shall I become a data analyst? …
ETL & Data Quality Test Cases & Tools: Examples
Testing the data that is being processed from Extract, Transform and Load (ETL) processes is a critical step in ensuring the accuracy of data contained in destination systems and databases. This blog post will provide an overview of ETL & Data Quality testing including tools, test cases and examples. What is ETL? ETL stands for extract, transform, and load. ETL is a three-step process that is used to collect data from various sources, prepare the data for analysis, and then load it into a target database. The extract phase involves extracting data from its original source, such as a database or file system. The transform phase involves transforming this data …
Amazon Kinesis vs Kafka: Concepts, Differences
As technology advances, new data streaming solutions emerge to meet the ever-growing demand for real-time analytics. Two popular options are Amazon Kinesis and Apache Kafka. Here, we’ll take a look at these two platforms and compare them in terms of their core concepts and differences. What is Amazon Kinesis? Amazon Kinesis is an AWS serverless streaming service that allows you to collect, process, and analyze streaming data in real time. It is a fully managed service that can capture, store, and analyze hundreds of terabytes of data from millions of sources simultaneously. It is designed to be highly available and scalable so that your streaming data can be reliably processed …
Data Analyst Technical & Soft Skills
Do you want to become a data analyst? It’s a great career choice! Data analysts are in high demand these days. Companies rely on data analysts to help them make better decisions by turning data into insights. In order to be successful, data analysts need a mix of technical skills and soft skills. Technical skills include expertise in analyzing data. Soft skills include communication and problem-solving skills. Data analysts must be able to take data and turn it into insights that help their company make better decisions. They also need to be able to effectively communicate those insights to people who may not have a technical background. In this blog …
Data Lake: Design principles & Best practices
In today’s business world, data is king. The more data you have, the more insights you can glean about your customers, your products, and your operations. And the best way to collect and store all that data is in a data lake. A data lake is a data management and analytics platform that offers several benefits over traditional data warehouses. Data lakes have gained in popularity in recent years due to the growing volume of data being generated by businesses and organizations of all sizes. But before you can reap the benefits of a data lake, you need to design it correctly. The people who should be involved in designing …
Building Data Analytics Organization: Operating Models
Most businesses these days are collecting and analyzing data to help them make better decisions. However, in order to do this effectively, they need to build a data analytics organization. This involves hiring the right people with the right skills, setting up the right infrastructure and creating the right processes. In this article, we’ll take a closer look at what it takes to set up a successful data analytics organization. We’ll start by discussing the importance of having the right team in place. Then we’ll look at some of the key infrastructure components that need to be put in place. Finally, we’ll discuss some of the key process considerations that …
Data Analytics – Different Career Options / Opportunities
Data analytics career paths span a wide range of career options, from data scientist to data engineer. Data scientists are often interested in what they can do with the data that is analyzed, while data engineers are more focused on the analysis itself. Whether you’re looking for a career as a data scientist, data analyst, ML engineer, or AI researcher, there’s something for everyone! In this blog post, we will different types of jobs and careers available to those interested in data analytics and data science. What are some of the career paths in data analytics? Here are different career paths for those interested in data analytics career: Data Scientists: …
Data Quality Challenges for Analytics Projects
In this post, you will learn about some of the key data quality challenges which you may need to tackle with, if you are working on data analytics projects or planning to get started on data analytics initiatives. If you represent key stakeholders in analytics team, you may find this post to be useful in understanding the data quality challenges. Here are the key challenges in relation to data quality which when taken care would result in great outcomes from analytics projects related to descriptive, predictive and prescriptive analytics: Data accuracy / validation Data consistency Data availability Data discovery Data usability Data SLA Cos-effective data Data Accuracy One of the most important …
Data Science vs Data Engineering Team – Have Both?
In this post, you will learn about different aspects of data science and data engineering team and also understand the key differences between them. As data science / engineering stakeholders, it is very important to understand whether we need to have one or both the teams to achieve high quality dataset & data pipelines as well as high-performant machine learning models. Background When an organization starts on the journey of building data analytics products, primarily based on predictive analytics, it goes on to set up a centralized (mostly) data science team consisting of data scientists. The data science team works with the product team or multiple product teams to gather the …
I found it very helpful. However the differences are not too understandable for me