Data

A guide for managing a series of tubes.

Best Practices

• Avoid automatic retries for non-idempotent operations.
• Use at least once delivery with idempotent operations.
• Use at most once delivery with non-idempotent operations.
• Use stream processing for data which is not guaranteed to fit into memory.

Streaming

• Avoid storing messages with different schemas in the same topic or queue.
• Keep messages as small as possible.
• Use as few distinct resources (CPU, Postgres, S3, HTTP requests) as possible in each consumer.
• Use constant memory to process streams when feasible.
• When one message would perform more than one operation, instead have that message append a new message for each operation.
• When reading from the same queue with more than one consumer, ensure that it's acceptable to process messages from that queue out of order.
• When partitioning messages in a single topic, ensure that it's acceptable for messages from different partitions to be processed out of order.

Glossary

<dl> <dt>Data Engineering</dt> <dd> The task of building infrastructure and process for ingesting, processing, and aggregating data so that it can be displayed to users or made available to data scientists. </dd> <dt>Data Science</dt> <dd> The practice of using statistics, machine learning, and other tools to analyze data to discover trends and truths that can be used to provide business intelligence. </dd> <dt>Batch Processing</dt> <dd> Processing large amounts of data at once. This is acceptable for smaller amounts of data and can be simpler in terms of development and deployment. Some batch processes can also be useful for "recomputing the world" when you want to analyze existing data in a new way. </dd> <dt>Data Streaming</dt> <dd> Processing data in small chunks, one at a time, rather than processing all data at once. Streaming is necessary for processing infinite event streams. It's also useful for processing large amounts of data, because it prevents memory overflows during processing and makes it easier to process data in a distributed or real-time manner. </dd> <dt>Real-time</dt> <dd> Analyzing data and delivering results simultaneously so that stream output is always visible. For example, real-time analytics will mean that the system is constantly processing events (clicks, purchases, etc) and displaying the latest results in a user interface. </dd> <dt>Parallel Processing</dt> <dd> Performing multiple tasks at the same time, for example on different cores or processors. Parallel processing is necessary in order to perform more than one computation at once; common uses are parsing or aggregation. </dd> <dt>Concurrent Processing</dt> <dd> Managing multiple ongoing tasks at once without necessarily processing more than one task in the same exact moment. Concurrent processing is required in order to perform more than one effect at once, such as waiting for multiple network requests to complete. </dd> <dt>Constant Memory</dt> <dd> Processing a stream where the amount of memory required does not increase with the size of the stream. </dd> <dt>At Least Once Delivery</dt> <dd> A guarantee that a given message will be delivered at least once, but may be delivered more than once. This is achieved in Kafka by committing an offset after it's been fully processed and in RabbitMQ by acknowledging a message after fully processing it. </dd> <dt>At Most Once Delivery</dt> <dd> A guarantee that a message will never be delivered more than once, but may not be delivered at all. This is achieved in Kafka by committing an offset before fully processing a message and in RabbitMQ by acknowledging a message before fully processing it. </dd> <dt>Distributed Data Processing</dt> <dd> Breaking up data into partitions so that large amounts of data can be processed by many machines simultaneously. </dd> <dt>Cluster</dt> <dd> Several computers (or virtual machines) grouped together to perform a single task. </dd> <dt>Scala</dt> <dd> A programming language (like Ruby, Python, or JavaScript) which is fast and has become popular for data-focused tasks. Scala runs on the Java Virtual Machine, which is a high-performance engine for running languages like Scala that compile into bytecode. </dd> <dt>Type Safety</dt> <dd> Languages that provide type safety (such as Scala) check the program for possible errors as part of the compilation process, which allows developers to prevent many types of bugs before being deployed. </dd> <dt>Spark</dt> <dd> A distributed computing engine for big data and data streams. Spark is a Scala-focused framework for data engineering and data science. </dd> <dt>Kafka</dt> <dd> A distributed commit log for data streams. Many of the large data systems deployed today use Kafka. </dd> <dt>Record Stream</dt> <dd> A stream where each message is an independent, unique record which does not replace a previous record in the stream. </dd> <dt>Changelog Stream</dt> <dd> A stream where each message represents the latest state for a particular entity. </dd> <dt>Topic</dt> <dd> In Kafka, a partitioned, append-only log of messages which can be consumed in order by partition. </dd> <dt>Partition</dt> <dd> In Kafka, a way of breaking the messages of a topic into groups which can be consumed in parallel by one or more workers. </dd> <dt>Queue</dt> <dd> In RabbitMQ, messages sent to an exchange are placed on a queue. Messages on a queue can be consumed in parallel by one or more workers. </dd> <dt>Consumer</dt> <dd>An application or process that reads from a data stream.</dd> <dt>Producer</dt> <dd>An application or process that writes to a data stream.</dd> </dl>