How do you analyze data in real-time?

An example of things to consider where data source is web app ( API or other ). I would focus on basic validation, compliance and lose coupling. Use Schema registry for basic validation, marking sensitive PII data for encryption before propogating to downstream. e.g. True False or 1, 0 ? Data compliance is becoming important aspect of data engineering; thing about it from day 0. Building exactly once semantics is hard so consider using using Kakfa/ MQTT for such needs for data integrity and lose coupling.

One of the important consideration before ingestion data is to agree on "data contracts" with upstream data providers. This provides kind of a security shield against any data format or schema change. Through applying those kind of contracts, data providers notify data engineers in advance for any data format changes and data engineers can take preventive measures against unintended consequences in data pipeline.

Dealing with data's volume, velocity, and variety demands a repertoire of methods and protocols such as HTTP, MQTT, Kafka, and Flume. ???? Preprocessing steps like validation, compression, encryption, and transformation refine raw data into actionable insights, ensuring data quality and security. By mastering the art of connecting to data sources, we lay the foundation for impactful real-time analysis that reshapes industries and drives innovation. Let's embrace the possibilities of real-time data together! ???? #RealTimeData #DataSources #TechInnovation Feel free to engage and share your insights below! ?????

Server-side data collecting is dependent on the business architecture. E.g., if the available CPU resource of the system is sufficient, we can use almost any mature tool to collect data and make initial filtering in place, such as Flume, Logstash, Filebeat, etc. In practice, it's better to separate the data filtering from data collecting since it's essential to make the smallest influence on the business servers. So using a C++/go-based agent like Filebeat would be a better choice for pure data collecting, then transferring data to a buffer cluster to preprocess and filter the data. Client-side data collecting should consider the instability of the user's network and the implementation of event tracking and log formatting, usually by an SDK

I'd also like to add that the way to interact with a data source is highly important. You need to use a data orchester to make sure you data is up to date and periodically check for changes. Google Cloud Composer is a good way to periodically pull data from different sources, and my personal favorite AWS Lambda and the CloudWatch functions can help keep everything up to date. There are also third party softwares that are great tools if you wish to integrate directly with a BI dashboard and not have to go through a database or other proxy

Spark Streaming and Storm are popular before 2015s, which respectively represent the method of micro-batch processing and event-level real-time processing. After 2015, flink became one of the most popular stream-processing frameworks because of its sub-second low latency, high throughput, various advanced APIs, good SQL support, and excellent fault-tolerance and failure recovery mechanism.

Due to some unexpected reasons such as network problems, malfunctions or faults in data producers, data might arrive at a data pipeline in a disordered manner and in such cases it needs to be handled properly. Therefore, for such scenarios we need to implement a scalable data buffer medium which is generally a message broker such as Kafka. This will enable data engineer dealing with delayed data properly. Many cloud providers offers managed version of such or similar systems like Google Cloud's Pub/Sub.

Scalability: It's essential to ensure that the chosen framework can handle the expected volume of data. Latency Requirements: Understanding the real-time requirements is key to selecting the appropriate processing framework and tuning the system. Data Quality and Consistency: Proper validation and handling of erroneous data are crucial to prevent false alarms. Security and Compliance: Ensuring that the system meets regulatory and security standards is a non-negotiable aspect. Continuous Monitoring and Improvement: Keeping an eye on the system and continuously improving it based on feedback and changing requirements ensure ongoing success.

Adding my perspective to focus on basics. Scalability - Consider partitioning and parallelism when dealing with high throughput streams. Fault Tolerance - Use checkpointing and replay to ensure correct results despite failures. Schema Evolution - Design systems to adapt to changing data formats and needs. Data Quality - Validate early to avoid cascading bad data but retain for analysis. Use dead letter sinks. Integration - Build connectors to enable a variety of data storage options. Monitoring - Implement logging, metrics and tracing to debug failures and optimize performance. Complexity - Modularize pipeline logic and leverage abstractions like windows to tame complexity like updates or joins.

I completly agree with this, stream processing is extremely important. There's a lot of information and most of it is not relevant to a given task. Not only it may take up valuable space but also clog up and add unwanted noise to a given analysis. It's important to have a very detailed and precise datamap when dealing with real-time data because it's very likely that as time passes, variables are renamed, collected differently and this can propagated to several systems. If you do not have a good datamap and closely monitor, you may only notice much later

Storing real-time can greatly benefit from strategies that prioritize speed, scalability, and accessibility. Employing in-memory databases like Redis can offer rapid data retrieval for frequently accessed information. Additionally, a hybrid approach that combines real-time databases with traditional databases ensures a balance between real-time processing and long-term storage needs. Regular data archiving and cleanup routines are essential to maintain database performance and prevent unnecessary data accumulation.

Data storage design depends on usage and business needs. I follow these guidelines: Retain raw streams in a central store like HDFS before processing to provide an audit trail and drive other use cases. Ingest only needed columns or time ranges into downstream caches and time-series databases to avoid duplication. Use a tiered architecture matching storage performance and cost to data value. Fast SSDs for hot data, slower object storage for colder data. Use partitioning typically time partitioning, common access key (i.e. customer key for B2B business) etc. Set retention policies balancing business value and cost. Archive or delete old data to save resources.

The most part of data storage is actually data modeling because raw data can hardly be used for users. We usually construct our data warehouse with three layers. The first layer involves filtering, verifying, deduplicating, and refining the raw data. Then, the refined data will be stored in the ODS database. The second layer will consider using different data models to join and aggregate the event-level data into aggregated tables based on the universal topic of the business, and the result will be stored in the DW database. The third layer will further aggregate the data from the second layer based on expert knowledge and business requirements and stored in the ADS database.

You definetly need a robust data architecture if you want to handle the amount of data incoming. I usually recommend an operational database with short-term data ( how short depends on how much data we're talking about ) and then move into a data lake or data warehouse

Data Querying is the most important for all data engineers. SQL (Structured Query Language) is one of the key tools used by data engineers to create business logic models, execute complex queries, extract meaningful information, write analytics queries and build reusable data structures. PostgreSQL - The most popular open-source relational database. MongoDB - popular NoSQL database. Apache Spark - An open-source analytics engine known for its large-scale data processing capabilities. Snowflake - popular cloud-based data warehousing platform. Big Query - is a powerful tool that lets you analyze massive datasets without worrying about the infrastructure. Apache Hive - an open-source data warehouse, provides a SQL-like language called HiveQL.

Because of the volume of information from large data sources like a satellite image, how the volume is collated and organized is more important than the actual dissemination of the information contained in the imagery. And because AWS services provide for seamless integration of Cloud services, using an AWS based architecture makes a lot of sense and adds incredible value to the processing stage. Using an Amazon SNS Topic with an Amazon SQS queue to subscribe to it and store to a Amazon S3 bucket with minimal duplication of data, in a polled and phased manner, even when the volume scales up, helps in acceleration of the processing stage. And with the help of a querying engine like Athena, fine tuning the processing, too, gets simpler.

A prime example of a powerful query engine is Google BigQuery, a fully managed data warehouse that seamlessly integrates with streaming sources. BigQuery empowers users to compose SQL queries that traverse streaming data with lightning speed, enabling real-time aggregations, joins, and complex analyses. The ability to swiftly gain insights from live data opens avenues for responsive decision-making and situational awareness.

Data querying again depends on the use case and but I really like modern query engines like Trino, Druid etc provides excelled capability to not only decouple but connect variety of data sources. They also provides the interface through which customers of data system will interact. I often use couple of techniques to further using two techniques; 1) consider query API if the use case is highly available or production facing interactive system 2) Use database view to hide complex tables or joins that standardize performant joins and allows minor changes underneath like column name change or format change ( date format ).

Here are some of the best practices for implementing data pipelines: 1. Data Quality: Ensure clean, validated data input. Modular Design: Build reusable, scalable pipeline components. 2. Automation: Schedule and trigger processes automatically. 3. Monitoring: Monitor performance and set up alerts. 4. Error Handling: Plan for errors and implement retries. 5. Security: Encrypt data and enforce access controls. 6. Version Control: Track changes to code and configurations. 7. Documentation: Maintain detailed pipeline documentation. 8. Testing: Thoroughly test components and validations. 9. Resource Management: Optimize hardware and parallelism. 10. Data Lineage: Record data journey for auditing.

Here's a condensed guide to initiate your journey into data pipeline orchestration within the GCP ecosystem: -Leverage GCP's Robust Services:Services like Google Cloud Dataflow, Cloud Composer (built on Apache Airflow), and Cloud Dataprep offer versatile capabilities for orchestrating, transforming, and processing data. -Comprehend Data Flow: Begin by comprehending the flow of data within GCP. Identify data sources, processing stages, and destinations. This understanding forms the foundation for effective pipeline design.

The critical yet often overlooked aspect of a data pipeline is the meticulous orchestration and synchronization of various stages. A seamless flow that ensures data integrity, consistency, and efficient transformation is the unsung hero that truly empowers meaningful insights. Achieving seamless orchestration in a data pipeline involves thorough 1. planning, 2. utilizing robust tools for automation 3. closely monitoring data quality, 4. implementing error-handling mechanisms. 5. Regularly reviewing and optimizing the pipeline ensures a smooth flow and reliable insights.

Data quality can be affected by various factors, such as data sources, data processing, data storage, data querying, and data pipeline. To ensure data quality, you need to implement various measures and practices, such as data validation, data cleansing, data auditing, data testing, and data governance. -Accuracy Matters -Completeness Check -Consistency Across Sources -Timeliness -Adhere to Standards -Identify Data Source Reliability -Data Processing Integrity -Effective Data Cleansing -Audit Trails -Data Testing Strategies -Data Governance Framework -Regular Data Validation -Cross-Validation

Besides excellent points already mentioned I think Centralized Schema Registry that ties togeather all data systems across the organization is key to achieve high data quality along with fault tolerance design. In past I have used AVRO schema ( with custom tags ). Apart from technical help; quality also needs some human intervention IMHO. So creating a simple, easy to follow process for data management is important. i.e. form a committee to review new data tables, columns before jumping through it. Create guidelines for naming convention, capacity analysis and compliance.

One thing to ass here is data security. The target audience and the level of their access for the data needs to be defined. It is always to good to have PII anonymized or delete unless the downstream requirements has a strong a reason not to do so.

It is good merging data engineering scopes with business analytics objectives and data scientists approaches. I mean, what we talk about here is about processing data. But analyzing data involves many other things, that of course can be done online. Accuracy, cross-validation, changes in distribution or patterns is more than just ETL or ELT. You may have a solid team of professionals to handle all of these events. Another issue not covered by the article is security. How secure is your data architecture workflow? Are there back-doors granting data poisoning? May your data patterns tell something about security?

Data Contract - A data contract is a formal agreement between two parties, the data provider and the data consumer. A data contract outlines how data can be exchanged between two parties. It defines the structure, format, and rules of exchange in a distributed data architecture. These formal agreements make sure that there aren’t any uncertainties or undocumented assumptions about data. Despite an intimidating name, data contracts aren’t as complicated as they first seem. And they can be incredibly helpful for improving accountability across data assets.

Following these steps would enable an organization to technically perform real time analyses. But moving from analyses to conclusions and to real time corrective action suggestions and improvements is a major challenge. First, the organization needs to be geared up to action on the historic analyses, before you can derive value from real time analyses. Organizing your team around the PDCA circle is a good start to instigating continuous improvement, including a significant amount of business expertise as well.

A data contract is an agreement between the producer and the consumers of a data product. Just as business contracts hold up obligations between suppliers and consumers of a business product, data contracts define and enforce the functionality, manageability, and reliability of data products. the ability to define and validate data contracts is essential for cross-team collaboration. With data contracts: The producer of a data product interface can ensure that it is not accidentally causing breakages downstream which it did not anticipate. The consumer of the interface can ensure that it is not and will not be broken.