A Survey of 21 ETL Tools for Python

Here are summaries of each of the tools you’ve mentioned along with examples of how to implement the ETL (Extract, Transform, Load) process using each tool within a Python workflow:

  1. Apache Spark: Apache Spark is a powerful open-source cluster-computing framework that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It’s commonly used for processing large-scale data and running complex ETL pipelines. Example Implementation:
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("ETLExample") \

# Load data from source
source_data = spark.read.csv("source_data.csv", header=True, inferSchema=True)

# Apply transformations
transformed_data = source_data.select("column1", "column2").filter(source_data["column3"] > 10)

# Write data to destination

  1. Apache Airflow: Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It allows you to define complex ETL workflows as directed acyclic graphs (DAGs) and manage their execution. Example Implementation: Define a DAG in a Python script:
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def etl_process():
    # Your ETL logic here

default_args = {
    'start_date': datetime(2023, 8, 1),
    'schedule_interval': '0 0 * * *',  # Run daily at midnight

dag = DAG('etl_workflow', default_args=default_args)

etl_task = PythonOperator(
Continue reading “A Survey of 21 ETL Tools for Python”

Designing a Relational Database – Data Modeling

Designing a system against a relational database involves several key elements that ensure data integrity, efficient query performance, and maintainability. Here’s a summarized overview of these design elements: This article I am going to strive to cover the first of numerous key elements of designing a system against a relational database. I will eventually cover the following topics, but this post will specifically be based on the first topic data modeling:

Data Modeling

The general idea with data modeling for a relational database (and for other types of databases) is to build the database in a way that caters to your specific usage needs. This involves multiple layered tasks. Each of the sections below I’ll define simple the task, then elaborate on characteristics of that particular task.

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This is a getting started guide for MariaDB SkySQL. Let’s start with two prerequisites definitions:

  1. MariaDB – MariaDB is an open-source relational database management system (RDBMS) that is a fork of MySQL, another popular open-source database system. It was created by the original developers of MySQL after concerns arose about the acquisition of MySQL by Oracle Corporation in 2010. MariaDB is designed to be a drop-in replacement for MySQL, which means that many applications and tools developed for MySQL can also work seamlessly with MariaDB without requiring significant changes. It retains much of the same syntax, APIs, and commands as MySQL, making the transition relatively straightforward for users.
  2. MariaDB SkySQL – MariaDB SkySQL is a cloud-native Database as a Service (DBaaS) offering provided by MariaDB Corporation, the company behind the development of the MariaDB open-source database system. SkySQL is designed to simplify database management, deployment, and scaling by providing a fully managed and highly available MariaDB database solution in the cloud.

Some key features of MariaDB include:

  1. High Performance: MariaDB incorporates optimizations and improvements to enhance query execution speed and overall performance.
  2. Storage Engines: MariaDB supports multiple storage engines, including the popular InnoDB and Aria engines. Each engine has its own characteristics and performance attributes, allowing users to choose the one that best fits their requirements.
  3. Security: MariaDB includes various security enhancements, such as data encryption at rest and in transit, improved authentication methods, and better access control mechanisms.
  4. Open Source: MariaDB is fully open source, which means its source code is available for anyone to inspect, modify, and contribute to.
  5. Community and Development: MariaDB has a vibrant and active community of developers and contributors who work on its continued development and improvement.
  6. Compatibility: As mentioned earlier, MariaDB aims for compatibility with MySQL, allowing applications developed for MySQL to work with minimal changes.
  7. Extensions: MariaDB introduces some features not present in MySQL, such as the Aria storage engine, thread pooling, and more advanced geographic information system (GIS) functionality.
  8. Replication and Clustering: Like MySQL, MariaDB supports various replication methods and clustering solutions for high availability and fault tolerance.
  9. Plugins: MariaDB offers a plugin architecture that allows users to add custom functionality and features to the database system.

To elaborate further on the specifics of MariaDB SkySQL, here are some of the features of the DBAAS (DataBase As A Service):

  1. Managed Service: SkySQL takes care of database administration tasks such as provisioning, backup, monitoring, maintenance, and security updates. This allows users to focus more on their applications and less on managing the underlying database infrastructure.
  2. High Availability: SkySQL offers built-in high availability configurations that ensure database uptime and data durability. This includes automatic failover and replication setups.
  3. Scalability: SkySQL supports both vertical and horizontal scaling. Vertical scaling involves adjusting the resources of a single database instance, while horizontal scaling involves distributing data across multiple nodes for improved performance and capacity.
  4. Security: Security features such as encryption at rest and in transit, role-based access control, and network security protocols are integrated to help protect sensitive data.
  5. Multi-Cloud Support: SkySQL is designed to work across various cloud providers, enabling users to choose the cloud environment that best suits their needs. It supports popular cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
  6. Compatibility: SkySQL maintains compatibility with the MariaDB database, which means applications developed for MariaDB can run seamlessly on SkySQL with minimal modifications.
  7. Global Distributed Architecture: SkySQL offers the capability to deploy databases across multiple geographic regions for improved performance and data availability across different parts of the world.
  8. Managed Upgrades: Regular updates and improvements to the MariaDB database engine are managed by the SkySQL service, ensuring that your databases remain up to date without manual intervention.
  9. Pay-as-You-Go: SkySQL’s pricing model is typically based on usage, allowing users to pay for the resources they consume. This can be cost-effective for businesses as it eliminates the need to invest in and maintain dedicated database infrastructure.
Continue reading “MariaDB & SkySQL DBAAS”

Shortlist of Database as a Service Providers

Some top database providers for various open source databases like MariaDB, PostgreSQL, MongoDB, Apache Cassandra, Redis, Elasticsearch, and Neo4j:

  1. MariaDB:
  2. PostgreSQL:
  3. MongoDB:
  4. Apache Cassandra:
  5. Redis:
  6. Elasticsearch:
  7. Neo4j (Graph Database):

Top 10 GraphQL Anti-patterns IME “The Horror”

While GraphQL provides a flexible and powerful approach to building APIs, there are some common anti-patterns that developers may unintentionally implement when working with GraphQL query resolvers. These anti-patterns – the opposite of yesterday’s top 10 practices – can lead to issues such as performance bottlenecks, security vulnerabilities, or maintenance difficulties. Here are some of the top anti-patterns to avoid:

  1. N+1 Problem: The N+1 problem occurs when resolver functions trigger additional database queries within a loop or for each item in a list. This can result in a large number of database queries, leading to poor performance. Implement data batching techniques using tools like DataLoader to mitigate this issue, to learn more about DataLoader, check out this post.
  2. Over-fetching and Under-fetching: Over-fetching happens when a resolver fetches more data than the client actually needs, resulting in unnecessary data transfer and increased response size. On the other hand, under-fetching occurs when the resolver does not provide enough data to fulfill the client’s request, leading to additional round trips. Design your resolvers carefully to strike the right balance and only fetch the required data.
  3. Resolver Fatigue: Resolver fatigue refers to a scenario where a single GraphQL resolver is responsible for handling a large number of fields or complex logic. This can make the resolver codebase difficult to maintain, understand, and test. Break down your resolvers into smaller, more manageable units to avoid resolver fatigue.
  4. Deep Nesting: GraphQL allows for nested queries, but excessive nesting can lead to performance issues. Deeply nested queries may result in complex resolver logic and multiple database queries. Try to flatten your schema structure and optimize resolver logic to avoid unnecessary complexity.
  5. Lack of Caching: Not implementing caching mechanisms in your resolvers can result in repeated and costly data fetch operations. Introduce caching strategies, such as in-memory caching or distributed caches, to store frequently accessed data and reduce the load on your data sources.
  6. Inefficient Pagination: Pagination is commonly used in GraphQL to handle large datasets. Implementing pagination incorrectly can lead to performance issues and inefficient querying. Use appropriate pagination techniques, like cursor-based pagination, to efficiently retrieve and display data. To read more details on pagination and how it can be applied to GraphQL queries check out this post.
  7. No Rate Limiting: Without proper rate limiting mechanisms, your GraphQL API may be susceptible to abuse and DoS attacks. Implement rate limiting at the resolver or API level to control the number of requests and protect your server resources.
  8. Lack of Input Validation: Failing to validate and sanitize user input can lead to security vulnerabilities, such as SQL injection or unauthorized data access. Validate and sanitize input parameters in your resolvers to prevent these risks.
  9. Monolithic Resolvers: Creating monolithic resolvers that handle multiple unrelated responsibilities can lead to code duplication, reduced reusability, and increased maintenance effort. Follow the single responsibility principle and modularize your resolvers to improve code organization and maintainability.
  10. Insufficient Error Handling: Inadequate error handling in resolvers can result in unhandled exceptions or unclear error messages returned to the client. Implement comprehensive error handling and provide informative error messages to assist client developers in troubleshooting and debugging. For more details on error handling, check out this post.

By avoiding these anti-patterns and following established best practices, you can enhance the performance, security, and maintainability of your GraphQL query resolvers.