May 2021 Summaries

In this blog post, we explore how to use pgAdmin with PostgreSQL, a popular combination for database administration and development. We cover the creation of an Aiven PostgreSQL service, installation and connection of pgAdmin, loading a Netflix dataset into PostgreSQL via pgAdmin, and querying data using pgAdmin. The post highlights the benefits of using pgAdmin as a user-friendly interface for managing and exploring PostgreSQL databases.

M3 is a time series data platform designed to handle massive amounts of metrics scalability needs, as experienced by Uber. It consists of the M3 Coordinator, M3DB (a native distributed TSDB), and M3 Query Engine. The M3 Coordinator serves as a bridge between collection agents and storage in M3DB, while the M3 Aggregator handles stream-based downsampling of data before passing it to M3DB for storage. M3DB is optimized for storing time series data at scale, offering enormous storage and financial savings through compression work. M3Query performs just-in-time compression in its querying algorithm to reduce memory usage and works with a columnar data format in M3DB, allowing parallel execution of queries. M3 supports PromQL (from Prometheus) and Graphite functions for querying languages but is also working on releasing its own querying language (M3QL).

David Esposito, a solution architect at AivenTech, shares his top 5 tips for selecting a managed cloud infrastructure service provider. Firstly, have a strong opinion about vendor lock-in and consider the refactor cost of moving away from a platform. Secondly, define acceptable downtime and understand the implications of maintenance windows on uptime SLAs. Thirdly, know your limitations and ensure you have the right expertise for managing the chosen technology. Fourthly, research hidden costs such as networking fees and security upgrades. Lastly, plan for growth together with the service provider to ensure scalability and compatibility with new use cases.

This blog post aims to teach the basics of Apache Kafka Producers and Consumers through building an interactive notebook in Python using Jupyter Notebooks. The tutorial covers setting up a Docker container with JupyterLab, creating a Kafka instance with Aiven Console, and developing a Producer and Consumer for sending and receiving messages to/from the Kafka topic. It also provides additional resources for learning more about Apache Kafka and its related technologies.

Apache Flink is an open source framework for data processing that supports various languages, including SQL. This blog post provides a way to get Apache Flink's SQL Client as a set of Docker containers and includes an example of integrating a local CSV file with PostgreSQL using Flink's SQL pipeline. The tutorial also covers setting up Apache Flink locally with Docker Compose, unleashing the power of SQL, creating a data target in PostgreSQL, and wrapping up the process.

Apache Flink is an open source framework and distributed processing engine designed for stateful computations over unbounded and bounded data streams. It enables rapid data streaming, allowing businesses to transition away from batch processing in certain use cases. Companies can utilize Flink for simultaneous streaming and batch processing, handling millions of records per minute, powering applications at scale, and utilizing in-memory performance. This powerful open source program helps ensure powerful applications deliver modern user experiences at scale.