ByteHouse

Rules for writing good SQL For many #dataengineers , SQL is the language of choice. It is a powerhouse that allows to go through big chunks of data. But, if not used correctly, it can incur huge query quests. So, what do you need to do? Learn 'good' SQL. And the best way of learning is by doing. Here are a few rules to help you. #dataengineering #sql

On-Chain Analytics 📈 On-chain analytics refers to the analysis of data on the blockchain, which can provide valuable insights into transaction history, user behaviour, and network health. This data can be used to make informed decisions, identify trends, and detect anomalies in the blockchain network. Use Cases: 1. Monitor Network Health: On-chain analytics offers real-time monitoring of network health by detecting issues like congestion, downtime, or malicious activity. This ensures optimised network performance and pre-emptive attack prevention. 2. Analyse User Behaviour on the Blockchain: On-chain analytics tracks transaction history and patterns to analyse user behaviour. It distinguishes active from dormant users, predicting future transactions. This valuable insight enhances understanding of user behaviour and facilitates improved user engagement strategies. 3. Identification of Fraudulent Activity: On-chain analytics examines blockchain data to detect fraudulent patterns, safeguarding against activities like wash trading or market manipulation. It serves as a crucial tool in preventing fraud and protecting investor interests. 4. Improve Scalability of Blockchain Networks: By analysing network data, on-chain analytics identifies bottlenecks and issues limiting scalability. This information is instrumental in optimizing the network, ensuring improved scalability for blockchain networks. #dataengineering #dataanalytics #onchain #blockchain

Zero Trust in Data Engineering 🔐 What is Zero trust? Zero Trust is a security framework requiring all users, whether in or outside an organisation’s network, to be authenticated, authorised, and continuously validated for security configuration and posture before being granted or keeping access to applications and data. This model advocates a fundamental shift: ‘never trust, always verify.’ Zero Trust principles in data engineering: - Continuous Authentication: Real-time identity verification updates access rights based on trust levels, ensuring ongoing security. - Least Privilege Access: Limiting user/system access minimises potential harm and reduces entry points for security breaches. - Data Encryption: Encrypting data at rest and in transit provides a robust security layer, reducing the risk of unauthorised access in a world rife with cyber threats. - Micro-Segmentation: Dividing data pipelines into controlled units lowers targets for potential cyberattacks, minimising harm in case of a breach. Advantages of Zero Trust adoption: - Enhanced Visibility: Ongoing monitoring and behavioural analytics detect data access patterns, allowing proactive threat detection. - Enhanced Security: Continuous verification reduces the risk of data breaches, providing a resilient defence against unauthorised access. - Compliance: Zero Trust ensures robust data protection measures, facilitating compliance with strict regulatory requirements. #dataengineering #datasecurity

Deriving value from a Data Product in a Data Mesh context In the context of Data Mesh, Data Product, goes beyond mere functionality; it emphasises reliability and trustworthiness. For instance, a data warehouse providing raw data is a Data Product, but true value lies in its trustworthiness. Data Mesh builds upon this notion by defining a Data Product as more than just a tool—it encompasses the data itself, reflecting the transformed interpretation in the contemporary data landscape. To effectively use a Data Product, access to data quality indicators—freshness, completeness, consistency, and uniqueness—is crucial. Inspection of lineage, exploration of metadata, and knowledge of the accountable person are essential for understanding the data’s meaning. Without these, a source data asset falls short of being a true product. Trust and transparency are paramount in the data realm. Establishing a clear data product definition to derive value - Owner: Clear ownership for issue resolution and information. - Description: Complete dataset details for semantic understanding. - Quality Indicators: Accurate, complete, and fresh data indicators. - Lineage: Origins of the data for consumer awareness. - Sampling: Quick exploration through data sampling. - Visibility: All properties accessible via the Data Catalog. Establishing a clear definition of a Data Product encourages teams to view their data as valuable products from a consumer perspective, and enables consumers to utilise their data effectively. #dataengineering #dataproduct #datamesh

The challenges of semi-structured data In this insightful post, Daniel Beach talks about the challenges of semi-structured data. https://lnkd.in/gYMu8qaK #dataengineering #xml #json

This is such an elegant representation of data pipelines from Alex Xu of ByteByteGo #dataengineering #datapipelines

As this year comes to a close, ByteHouse wishes you and your loved ones more peace, love, good health, and success in 2024. Wish you a Happy and Prosperous New Year! #happynewyear

How is data warehousing adapting to accommodate the needs of Web3 In this blog post, we discuss the evolution of data warehousing to accommodate the Web3 and Blockchain space, its use cases, challenges, and solutions. https://lnkd.in/gqeyF8ii #dataengineering #datawarehousing #web3

Measuring data quality at Airbnb through Data Quality Score In this insightful article, Clark Wright shares how they developed the DQ Score, how it’s being used today, and how it will power the future of data quality at Airbnb. https://lnkd.in/ggDR4vkY #dataengineering #dataquality #airbnb

ClickHouse for large-scale data ingestion and application This article explores the application of large-scale data ingestion and use with Go and ClickHouse. Go allows for high-performance parallel processing, and ClickHouse is known for providing rapid aggregations, making it convenient for big data analysis. Read it here - https://lnkd.in/gxT7CCs5 #dataengineering #clickhouse #golang