Was sind die effektivsten Entwurfspraktiken für Data-Lake-Architekturen?

Data lakes fundamentally thrive on ELT - Extract Load Transform. This enables us to onboard new data to the lake without making any changes to it giving us ample room to play with data as we go up the layers. Design the lake in a tiered architecture as per your requirements. See what kind of use cases you need to meet and then integrate them to your lake. Raw data zone gives you a lot of power to onboard different data sources in your lake which then you can process in the upper tiers to make them consumable.

It’s important if you are designing a data lake you should store raw data. That’s the main focus of a data lake. If you ingest data with transformations you are wrong. That’s not a data lake maybe is a data warehouse. You can use medallion architecture with 3 layers. Bronze layer for completely raw data, Silver layer where data is generally cleansed and conformed into usable structures, sometimes referred to as minimal-viable-data-products. And gold layer where you store your entities in response of each use cases you have to get the data and use directly for dashboard, ML models or analysis.

The key advantage of a data lake is it is simple to land all kinds of data in at scale. If you put too high a barrier for what can go into your lake, you're better off opting for a structured data warehouse. Always work from raw inputs if you desire data pipelines that are idempotent and concurrent. The ability to decouple compute from storage is powerful.

💥 Remember that data lake zones are spaces to support specified operations or processes; you can optimize it to improve performance, But it doesn't deviate from their single purpose. So you can create zones by: 💻 Transformations case; medallion is a great example; you transform data to delivery content in data product like IA model, reports or BI cases. 📑 Support cases: you can have metadata or other data to audit or control other business data. 🔒 Special cases: privacy process like DSAR for example required a separated and restricted space.

Firstly, it is important to define a clear data lake strategy, including the purpose, goals, and expected outcomes of the data lake. Secondly, designing a scalable and flexible architecture that can accommodate various data types, formats, and sources is vital. This includes the use of scalable storage solutions and leveraging technologies like Hadoop or cloud-based platforms. Additionally, implementing proper data governance practices, including data security, access control, and metadata management, ensures data quality and compliance. Lastly, establishing data ingestion and processing pipelines with automated workflows streamlines data ingestion and makes processing more efficient.

💥Remember when you create transactional data lake, choose right data file format and data table format. Some data table format support specific data file formats. Data File format are how define your data value and data structure; like CSV or XML files. 📙 Parquet: column focus, good in reading ops and better in compresion files. 📘 AVRO: row focus, good in writing ops and good in compresion files. 📗 ORC: column focus, good in writing ops and best in compresion files. Data table format are formats to control ACID operations: 🗃️ Delta lake: for batch ingestion and data lake. 🔍 Iceberg : for data warehousing and analytics processes. 🔀 Hudi: for streaming and real time ingestion.

You need some considerations before choose one format to store your data. But mainly you need to start thinking how you are going to use your data. How your going to write, Does is write heavy?, how you will read. Does is read heavy? Does is for analytical use? Or does is read/write heavy? Then with those considerations you need to know how each format works. Advantages and disadvantages. Does is columnar storage or row-oriented? Or if you need support for ACID transactions, you may consider Delta format. To summarize. You need to know how you will use your data before choose one format.

Choosing the appropriate data format also relies on the existing environment. If there are legacy systems that need to directly access data from, for example, the Silver-Zone (Medaillon Architecture), this factor must be taken into account when making decisions. Therefore, the selection of the appropriate data lake storage format is contingent not only on storage and processing capabilities, but also on the system context of legacy systems.

Consider these when choosing an Enterprise Data Catalog: 1. Integration - A data lake's in-built metadata management might seamlessly integrate with other components within its own ecosystem. However, a 3rd-party tool might offer broader compatibility with various data sources outside the data lake, enabling a more comprehensive metadata view. 2. Functionality - Data lake's OotB functions may cover basic cataloging needs, while 3rd-party tools might offer advanced features like data lineage, data quality checks, and customizable metadata tagging etc. 3. Cost - Using data lake's built-in functions might be more cost-effective initially, but 3rd-party tools might offer a more comprehensive solution, potentially justifying their higher cost.

💯 Your data lake metadata management is the key point in this process, because; a difference from databases; it requires to process to verify schema, access, structure and usage all data, if apply. ✔️ Govern level of privacy and security required. ✔️ Monitoring which data are common usage. ✔️ How data is generated and track this process. ✔️ Which business processes are related with what data. Choose right tool can support ; at minimum; business, operational and technical metadata.

1. Enforce strict security measures, including data encryption, access controls, and audit logging. 2. Be compliant with data protection regulations (e.g., GDPR, HIPAA). 3. Use the features on your warehouse technology - For example : Snowflake allows you to set data retention policies at the table level, enabling legal compliance by deleting records within the required timeframe for GDPR. Snowflake’s Access History feature tracks data subject requests, including access, deletions, and modifications, supporting the accountability and reporting requirements of GDPR and HIPAA

✨In general; all storage give set of security and governance features, so you need to evaluate this set is enough to work or need complementary tool to support more feature: so it depend of business requirements. So understanding the critical level of governance and security requirements that organization need to start your data lake project is basic.

Securing data is the need of the hour with data lake architecture, it’s very important to enable row level access controls to limit access to the users, data lakes should be designed considering access control, auditing and governance best practices.

Some of the best ways to optimise your data lake performance is - define retention periods, while you would like to keep data as long as possible, you no need to use costly / high performance zones. - staging data need not stay for long. Your raw data should stay in cheap storage. And your processed data should stay in high performance storage. Staging should be purged once it is processed. - while based on industry ( financial/ healthcare/ insurance) certain data is more queried like month end / year end etc. so optimise them accordingly. - based on queries , format should be chooses like columnar or wide row formats.

Use lifecycle management to automate handling of data based on predefined policies. Regularly monitoring and tuning the system based on usage patterns can help identify and address performance bottlenecks too.

Prior to commencing the optimization of data lake performance, it is essential to collect the necessary performance characteristics. There are numerous methods for optimizing performance, such as data tiering, data access tiers, partitioning, z-ordering, and more. However, it is important to determine in advance the specific performance requirements to deliver the right "product".

📋 Understand your business processes generate your data and how you need to structure it for data products, so if you want to improve your performance, you need: ⚡ Separate transforming data from business data: last one use in data product, and first one in exploring and building process. ⚡ Detect key fields can use to data partition, sharding or index strategies. ⚡ Define how ingestion process work to get data organized: how create and location your data affect performance. ⚡ Check you all process and storage performance accept general performance.

Reduce administrative burdens and unlock deeper insights by automating data lifecycle policies and implementing an observability layer. Streamline data management with automated retention, deletion, and archival policies, freeing your team to focus on higher-value tasks. Gain a holistic understanding of your data lake's health and performance by leveraging an observability layer, allowing for proactive identification and mitigation of bottlenecks. This will add more value to the environment to gain more insight, performance.

Data purning or purging policy You need to have data purging or pruning policy in place for old or historical data e.g data which 5 years old. This will make sure we do not store unwanted data. This will keep storage cost under control.

There are plenty of technologies available to design and define data lakes. However, one thing that it is very important is the overall cost associated with the design. For example, the data lake main contain unstructured data such as audio or image with requires attention to both storage and processing power (usually GPU). Therefore, when designing a data lake, it is imperative to go deep into the raw data involved early on to address technical, domain, and cost even before write the first line of code.

A data lake architecture offers distinct advantages compared to a traditional warehouse. One major benefit of a data lake architecture is the ability to separate storage and computing infrastructure, enabling greater scalability and cost savings through pay-as-you-go storage models. Traditional warehouses, particularly MPP-Architectures like Azure Synapse, deliver strong scalability and data processing capabilities for structured data.