What are the most common ETL pipeline patterns for ERP systems?

Batch processing, a widely adopted ETL approach, is particularly useful when there's no urgency in delivering data promptly to downstream systems. Its user-friendly nature shines through as users gain control over not just the batch schedule but also the ability to customize pipeline scripts. This flexibility ensures optimized configurations tailored to unique requirements. This method promotes efficiency by allowing the consolidation of data for processing in manageable chunks, making it easier to monitor and troubleshoot.

it is going to be a combination of Batch ETL and streaming ETL based on Business Case Scenarios. If Data is critical to the business, then better to go with real-time or near-real-time and if not so critical, then go with batch refresh. SAP's way of analytics is quite clear : 1 is Operational Analytics 2. Strategic & tactical analytics. For operational Analytics it is very much Real Time and for strategic & Tactical Analytics it is very much Batch Concept daily weekly etc., and may be monthly for Master data. In every case performance parameters need to considered for better/faster reporting and analytics.

Tudo irá depender dos requisitos do negócio e o objetivo para a qual os dados est?o sendo ingeridos. O ETL em lote é uma excelente op??o na maioria dos cenários mas é preciso ficar atento com rela??o a frequência de atualiza??o dos dados e custos do pipeline. Tudo precisa estar em harmonia com as expectativas da organiza??o e entrega de valor para as áreas de negócio.

Batch ETL simply means you're processing data in groups at scheduled intervals. So think of it like gathering a bunch of files and processing them together instead of one by one. This is particularly useful for processing smaller chunks of data as one large chunk at specific times. When you set up batch processing pipelines: - Determine the frequency i.e. work with stakeholders and establish how often they need the data updated e.g. daily, weekly, or less frequently - Plan for latency i.e. the lag between capturing data and making it available - Justify the efficiency you gain from it i.e. you must be able to do a cost-benefit analysis to determine whether the efficiency gained from this justifies the delay in the data availability

It's like gathering a bunch of files and processing them together, much like doing a load of laundry. You collect everything, wash it in one go, and then it's all done together.

Think of this like a continuous flow, where data is processed in real-time, more like a live broadcast than a recorded show. It's immediate and constantly updating.

Streaming ETL, is a dynamic approach within ERP systems, resembling a continuous flow rather than a traditional batch process ??. Unlike its batch counterpart, streaming ETL processes data in real-time, ensuring that the most up-to-date information is available for analysis and decision-making ??. This pattern leverages technologies like Apache Kafka or AWS Kinesis to ingest, process, and deliver data on-the-fly. As data continuously trickles in, transformations are applied instantaneously, providing businesses with timely insights. Streaming ETL is particularly beneficial for scenarios where immediacy and responsiveness are paramount, fostering agility and enabling organizations to adapt swiftly to changing conditions ??.

Streaming ETL is used to process the real time data using Spark Streaming and Kafka in most of the projects where we get to work with near real time data that we get from mobiles, IOTs, and other real time sources and handle them using spark streams. Kafka is a message broker and used mainly as a data integration platform. Example - Credit card fraud detection, page view analysis like who is viewing the page and what are they viewing and how much time are they viewing…Also uses in recommendation systems like Netflix

A streaming ETL processes data as it comes from the source, like a live feed. More companies are depending on this to understand their customers in real-time, so technologies like Kafka, Spark, and Flink provide support for real-time data streaming. Here's what to consider for stream-based pipelines: - Determine if you really need this. You need streaming pipelines if your operations are time sensitive and delays are NOT an option - Develop strategies to monitor and maintain high data quality because there's less control here than batch processing - Put mechanisms in place to recover data when failures occur, otherwise you will suffer data loss

Streaming ETL is designed for handling real-time data streams from ERP systems. It continuously extracts data as it becomes available, transforms it in real time, and loads it into the data warehouse or lake.

Hybrid ETL, like a data dance ??, seamlessly blends both batch and streaming ETL processes within ERP systems, creating a harmonious flow of information. ?? Batch ETL handles large volumes of data in scheduled chunks, ensuring orderly updates and maintenance, akin to a scheduled cleaning spree. ?? On the flip side, streaming ETL cha-cha-chas in real-time, reacting swiftly to incoming data like a lively dance partner, ensuring the ERP system stays in sync with the dynamic business rhythm. ?? This hybrid approach combines the best of both worlds, ensuring a synchronized ERP performance that's as smooth as a well-choreographed routine, accommodating the varied needs of modern data processing. ????

This one's like a mix of both worlds, combining the batch and streaming methods. It's versatile, allowing for both scheduled and real-time data processing.

Hybrid ETL is simply batch and real-time processing combined into one. Platforms like Databricks support this approach with Spark Streaming, but research and use the tools and technologies that suit your use cases

Hybrid ETL combines batch and streaming ETL approaches to achieve the best of both worlds. Batch ETL is used for processing large volumes of historical data, while streaming ETL handles real-time data streams.

Imagine it as a diligent data lifeguard, rescuing valuable information from the vast ocean of your business processes. In this pattern, data from ERP systems is extracted, refined, and then gracefully loaded into a centralized Data Lake. ?? The extraction phase involves scooping up relevant data from various sources within the ERP, ensuring nothing gets left behind. Transformation kicks in as the data undergoes a makeover, becoming sleek and standardized. Finally, the hero loads this polished data into the Data Lake, a secure haven where analytics and insights can flourish. ?? This pattern ensures your ERP data is not just swimming around aimlessly but is organized, ready to dive deep for meaningful business discoveries! ????

Imagine a vast reservoir storing all sorts of data. Data lakes store raw, unprocessed data, making it easily accessible for various analyses later on.

This method focuses on creating an agile and flexible storage system. It's like having a secure vault where you carefully organize and store your data for easy retrieval and analysis.

Data Vault ETL is a specific pattern designed for data warehousing, particularly for historical and dimensional data. It follows a three-tier architecture, separating dimensions, facts, and their relationships to ensure data integrity and traceability.

In the realm of ETL pipeline patterns, the Data Vault approach stands out by creating a robust and scalable foundation. Imagine it as the maestro orchestrating data harmony. ?? This method involves three key components: Hubs, acting as central repositories for core business concepts; Links, weaving relationships between these concepts; and Satellites, storing historical changes. It's like assembling a puzzle where every piece has its role, ensuring data integrity and flexibility. With Data Vault ETL, the ERP system transforms into a well-choreographed dance, gracefully handling data movements and changes without missing a beat. ???? It's the secret sauce for a reliable, future-proof data infrastructure! ???

Always do your research when selecting which ETL approaches you need for your use-cases. This will involve having deep conversations with stakeholders on - what data they need - how they want the data processed (type of ETL used) - how often they need it updated (frequency) ...among other important factors to create value for them. Don't just jump on streaming ETLs because everyone else is talking about it, make sure you perform your due diligence around them (or any approach you adopt) and justify the ROI and trade-offs for each before concluding on any decision.

I have been an advocate that the customer owns their own data. That is "data openness." It is my position that no data should be constrained by proprietary barriers, structures, definitions, or techniques. Software solution providers engage in the appropriation of customer data though the strategy of maintaining "stickiness" through proprietary lexicons, semantics, links, and tags. We also see this same model used in so-called BI and ad hoc analytical applications that require custom hard coding, and flattening of conditioned data from ERP and other systems that then require reconditioning. It's a great labor generating machine for the provider or reseller, but not sustainable, and constrains data utilization.

While these patterns are crucial, consider the scalability of the chosen pattern, its adaptability to changing needs, security aspects, and how it aligns with the specific requirements of your ERP system. This broader view helps in making a more informed decision.

The ERM of the ERP can be complicated, and in the case of SAP, the object names are obfuscated. By complicated, I mean 10,000 tables in what appears to be 4th normal form relational model. Technically you can write directly to the tables, but I have never seen this end well. An API or document route will offer the greatest reliability. SAP, for example, supports file and API for extraction and loading. Similarly, Microsoft Dynamics also has an API. The challenge with APIs is that they use HTTP, which isn't particularly performant. Whatever you do, keep your batches small. Large batches take time, impact system performance and are harder to recover from on failure. At least this is what I have found