Sign Up For The First Ever Low-Key Data Conference15 speakers. 4 hours. Virtual event. No frills. Join us on 3/22/23 from 1-5pm EST - Click here to sign up!.
Data sources are the places where you can obtain data for analysis. They come in various forms, such as data sets, APIs, software, and providers. Understanding data sources is essential for data analysis.
Ultimately, a dataset's quality and reliability heavily depends on the source from which it is obtained.
A data source provides access to a set of data that can be analyzed and gain data insights. Data sets are a popular way to store data in a structured format, making it easier to analyze.
Data sets are usually organized in tables. Here, columns represent different attributes of the data. Rows represent individual instances of the data.
To use a data source, you typically need to connect to it using a software tool or API. Once connected to the data source, you can extract the data you need for analysis and transform it into a data set.
Examples of data sources include public health data, Google Analytics and Linkedin.
Public health data is used to monitor the spread of diseases and predict future threats. Most businesses use
Google Analytics to track website traffic and user behavior.
LinkedIn provides data on user behavior, job market trends, and professional connections.
These data sources can provide valuable insights into consumer behavior and industry trends.
There are many more examples of data sources. It's best to find which is most suitable for your business needs.
Data can come from various sources, including data providers such as
APIs from popular SaaS software
Aggregate data sources (other people's data)
Data analytics tools
A data provider or an API allows developers to access data from web-based applications or services.
Aggregate data sources combine multiple long-tail data sources to provide a broader picture of a particular topic.
Data analytics tools are used to analyze data to gain insights into patterns or trends.
Combining data from multiple sources gives you a more comprehensive understanding of a particular topic.
Data visualization tools are essential when extracting data from multiple data sources. It helps to display data in a more understandable format. Data visualizations allow you to better identify patterns and trends.
These multiple data sources can be anything. For example, social media data can be analyzed to identify consumer sentiment or trends. Abstracts provide a summary of research papers or other documents. You can explore them to gain a deeper understanding of a particular topic.
Data sources can be categorized based on the structure of the data they provide. There are three primary types of data sources: structured, unstructured, and semi-structured.
Structured data refers to data with a specific structure, typically organized in a table format. Relational databases are a common source of structured data. They contain tables consisting of columns and rows.
SQL is a programming language used to manage and manipulate structured data. Microsoft SQL Server is a popular relational database management system that uses SQL. Structured data is widely used in finance, healthcare, and retail industries.
Unstructured data refers to data that doesn't have a specific structure, making it more challenging to analyze. Examples of unstructured data include text, images, and video.
You can find available data in publicly available sources. Some examples are government databases, news articles, and social media.
Machine learning is often used to analyze unstructured data. ML can use algorithms to identify patterns and relationships. You can use a cloud-based data warehousing solution such as Amazon Redshift to handle large amounts of unstructured data.
Semi-structured data is a combination of structured and unstructured data. It has some structure but is also flexible, allowing for changes as needed. Examples of some popular semi-structured data are
XML - Extensible Markup Language is a language used for creating documents that can be easily read by both humans and machines, and it allows users to define their own custom tags and document schema structures.
CSV - Comma Separated Values is a plain-text format used to store tabular data, where each row represents a value and each column represents a field, with the values separated by commas.
These data source formats are crucial since they are used in the data ingestion stage of extraction.
|Google Dataset||A search engine for datasets||Google Trends Data, Google Books Ngrams, Google Public Data Explorer|
|Data.gov||The home of the U.S. government’s open data||Agriculture, Climate, Education, Finance, Health, etc.|
|Kaggle||A community-driven platform for data science and machine learning||Titanic: Machine Learning from Disaster, New York City Airbnb Open Data, etc.|
|FBI Crime Data Explorer||Access to Uniform Crime Reporting (UCR) data||Crime in the United States (CIUS), National Incident-Based Reporting System (NIBRS)|
|WHO Global Health Observatory||Data and analyses on global health priorities||Mortality and global health estimates, Disease outbreaks, etc.|
|AWS Open Data||Registry of open and public datasets on Amazon Web Services (AWS)||Sentinel-2, Landsat, NOAA, NASA, etc.|
|Open Science Data Cloud||Public data storage and computing resource||Galaxy Project, BRAIN Initiative, Biomedical research, etc.|
|National Bureau of Economic Research (NBER)||Economic research data and analysis||Business Cycle Dating Committee, Census Research Data Center, etc.|
|Open Secrets Datasets||Money and politics datasets||2016 election, lobbying data, outside spending, etc.|
|NOAA Open Data Dissemination Program||Environmental data and information||Climate, Weather, Oceans, Coasts, Fisheries, etc.|
|Federal Reserve Economic Database (FRED)||Economic time series data||Interest rates, exchange rates, money supply, etc.|
|COVID-19 Data Repository by Johns Hopkins University||Data and resources related to COVID-19||Global confirmed cases, deaths, recovered, etc.|
|Gapminder||Global development data and statistics||Life expectancy, income, education, environment, etc.|
|United States Census Data||Data on people and economy in the United States||Population, housing, business, education, etc.|
|Pew Research Center||Public opinion and demographic research data||U.S. politics, media, social trends, religion, etc.|
|Real Estate Data from Realtor.com||Housing market data and analysis||Home prices, sales, trends, mortgage rates, etc.|
|Social Security Administration Open Data||Data on social security programs||Retirement, disability, survivors, Medicare, etc.|
|U.S. Bureau of Labor Statistics||Data and analysis on labor market activity||Employment, wages, prices, productivity, etc.|
|Data.World Datasets||Collaborative data platform||World Development Indicators, USAID Activities, etc.|
|FiveThirtyEight Open Data||Data journalism and statistical analysis||Sports, politics, economics, culture, etc.|
|Nasdaq Core Financial Data||Financial data and analytics||Stock prices, financial statements, SEC filings, etc.|
|Redfin Housing Market Data||Real estate data and analysis||Home prices, sales, inventory, demographics, etc.|
|The Examiner Headlines||News headlines from The Examiner (2016 U.S. Election)||Headlines, article content, authors, dates, etc.|
|Spotify Dataset||Music and audio data and analytics||Audio features, user data, music industry data, etc.|
|World Bank Open Data||Global development data and statistics||Economic trends, housing, commerce, and health.|
Using best practices for data sources is critical for ensuring the accuracy, reliability, and security of your data. By following these best practices, you can minimize the risk of errors or breaches. This improves the overall quality of your data. Here are some best practices for data sources.
Naming conventions are essential for ensuring consistency across data sources. Use a descriptive data source name that reflects the data's purpose and content. This makes it easier to locate and understand data sources, improving data quality and minimizing errors.
A data catalog is a searchable inventory of all data sources available within an organization.
A robust data catalog is essential for ensuring the accuracy and relevance of data. It improves productivity and minimizes the risk of errors.
It should include an interactive and user-friendly interface. This UI can include a table of contents, search functionality, and detailed information about each data source.
You need to protect the sensitive data of your business. Possible threats to your data can be from unauthorized access, breaches, or cyber-attacks.
Data security plays an important part in data-related businesses. is essential for protecting sensitive information. The principles of data security include confidentiality, integrity, and availability.
Use a secure database management system to protect data from unauthorized access. it also ensures compliance with data protection regulations.
In industries such as healthcare, it's crucial to uphold data security principles when you work with a healthcare dataset.
Data quality is critical for effective data analysis. Raw data may contain errors or inconsistencies that can impact the quality of data analysis.
Sanitizing raw data before storing it improves the data quality and minimizes the risk of errors. Use data ingestion tools or data pipelines to transform and clean data, making it suitable for analysis.
On-premises database management systems are installed and run locally within an organization's infrastructure. Here are some examples of on-premises database management systems.
MySQL - MySQL is a widely used open-source relational database management system. It is known for its flexibility, scalability, and reliability. It is a popular choice for web applications. MySQL is used by companies such as Facebook, Twitter, and YouTube.
Microsoft SQL Server - Microsoft SQL Server is another relational database management system. It's designed to run on Windows operating systems. It is known for its scalability, reliability, and ease of use. It is an ideal solution for organizations that require complex query support, advanced security features, and integration with other Microsoft products.
Oracle - Oracle is a powerful and scalable relational database management system. It is widely used in large enterprises. Oracle provides a comprehensive suite of database tools for managing and securing data. This makes it an ideal solution for large organizations with complex data management needs.
PostgreSQL - PostgreSQL is a powerful and feature-rich open-source relational database management system. PostgreSQL is an ideal solution for organizations that require advanced data management features. While PostgreSQL costs nothing to start using, you will still have to bear the cost of maintaining it.
Cloud-based data warehouses are data storage solutions that run on cloud platforms. They provide flexible and scalable storage for large amounts of data. Here are some examples of cloud-based data warehouses.
Google BigQuery - Google BigQuery is a cloud-based data warehouse. It enables real-time analytics and processing of large datasets. It is an ideal solution if you need real-time data ingestion and metadata management capabilities. Google BigQuery also supports automatic scaling. This feature helps it to handle large volumes of data with ease. BigQuery pricing is both on-demand and flat-rate pricing
Amazon Redshift - Amazon Redshift enables fast querying and analysis of large datasets. Amazon Redshift also offers many features. Some examples are automatic scaling, data security features, and support for various data formats. AWS Redshift pricing is complex and it's different based on how you use it.
MongoDB - MongoDB is a cloud-based document-oriented database. It provides flexibility and scalability for unstructured data storage. MongoDB is suitable as a flexible data storage solution for unstructured data. Some examples are social media, IoT, and mobile applications.
Snowflake - Snowflake is a cloud-based data warehouse that enables instant and scalable access to data. If you require instant data access and the ability to scale up and down as needed, Snowflake is a good option. Snowflake also supports big data and machine learning, automatic scaling, and data-sharing capabilities. Snowflake pricing is based on the amount of compute resources you use.
Data management refers to the process of collecting, storing, processing, and analyzing data. ETL is a popular framework used in data management. It involves three main functions: data extraction, data transformation, and data loading.
These processes help ensure that the data is of high quality and can be used effectively. Data integration tools like ETL tools can streamline the data management process making it more efficient.
Data extraction is the process of retrieving data from different sources. These data sources can be databases, web services, or files. During the data extraction process, data is often collected from multiple sources. Then they are combined into a single data set. Extracted data can be raw and unprocessed and may require further data transformation before it can be used for analysis.
Data transformation is the process of transforming raw data to make it suitable for analysis.
You have to make several decisions when you do data transformation. Some of them are
What data needs to be transformed and in what way?
What tools and techniques should be used for the transformation process?
How much data should be processed at a time to minimize errors and ensure accuracy?
How often should the transformation process be run to keep the data up-to-date and accurate?
The data transformation process involves several steps such as those mentioned below.
Data mapping: Mapping the source data to the target data.
Data cleaning: Removing errors, duplicates, and inconsistencies from the data.
Data filtering: Selecting specific data to be transformed.
Data aggregation: Combining multiple data points into a single value.
Data enrichment: Adding new information to the data.
Data normalization: Converting data into a standard format.
Data pivoting: Rotating data from rows to columns or vice versa.
Data joining: Combining data from different sources into a single dataset.
Once the data is transformed, you need to send them to a destination database or data warehouse. This is called the data loading process.
Data loading is the final stage of ETL. In this process, you set up schemas, ensure data consistency and integrity, and optimize data storage.
Data loading can be done in several ways as shown below.
Bulk loading is the process of inserting a large volume of data into a database in a single operation.
Incremental loading is the process of loading only the data that has changed since the last load.
Partition loading involves breaking up large datasets into smaller partitions, which can be loaded individually.
Data pipelines are used to automate the ETL processes. Data pipelines can be customized using templates and can be designed to work in real-time.
Data pipelines improve data accuracy and consistency and reduce manual workload. Data pipelines can be built using various integration tools and platforms, depending on the organization's needs.
From there, your large dataset can inform data visualization tools — or at a minimum — produce quality data insights for your data science or business intelligence team.
One of the most important data integration best practices is to use the best tools available in the market. Portable is a comprehensive no-code ETL tool. It offers a wide range of features to streamline the process of collecting, processing, and analyzing data.
This platform is designed with data integration best practices in mind offering efficient data ingestion and ETL capabilities.
Extensive built-in data connectors for over 300+ hard-to-find data sources.
Custom connector development is available on-demand with fast turnaround times.
Supports popular data warehouses, such as BigQuery, Redshift, and Snowflake.
Ongoing maintenance of long-tail connectors at no additional cost.
24x7 monitoring, alerting, and support.