Looking for a guide to data lake architecture? Data lakes are a great way to store and manage large amounts of data, but they can be complex to set up and manage. In this article, James Serra provides a valuable overview of data lake architecture, the different components, benefits, and challenges of using this approach. It's a must-read for anyone who is considering using data lakes. Read it here: https://lnkd.in/e6DNCnwd #datalake #architecture #datalakehouse #dataengineering
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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
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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
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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
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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
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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
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This is such an elegant representation of data pipelines from Alex Xu of ByteByteGo #dataengineering #datapipelines
Data Pipelines Overview. The method to download the GIF is available at the end. Data pipelines are a fundamental component of managing and processing data efficiently within modern systems. These pipelines typically encompass 5 predominant phases: Collect, Ingest, Store, Compute, and Consume. 1. Collect: Data is acquired from data stores, data streams, and applications, sourced remotely from devices, applications, or business systems. 2. Ingest: During the ingestion process, data is loaded into systems and organized within event queues. 3. Store: Post ingestion, organized data is stored in data warehouses, data lakes, and data lakehouses, along with various systems like databases, ensuring post-ingestion storage. 4. Compute: Data undergoes aggregation, cleansing, and manipulation to conform to company standards, including tasks such as format conversion, data compression, and partitioning. This phase employs both batch and stream processing techniques. 5. Consume: Processed data is made available for consumption through analytics and visualization tools, operational data stores, decision engines, user-facing applications, dashboards, data science, machine learning services, business intelligence, and self-service analytics. The efficiency and effectiveness of each phase contribute to the overall success of data-driven operations within an organization. Over to you: What's your story with data-driven pipelines? How have they influenced your data management game? – Subscribe to our newsletter to 𝐝𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐞 𝐆𝐈𝐅. After signing up, find the download link on the success page: https://lnkd.in/eawsYGiA #systemdesign #coding #interviewtips .
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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
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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
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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
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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
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Fantastic overview, would also be interested in your take for enabling data mesh concepts (products and domains) to each layer or mix and match approaches.