What do you do if your data architecture involves integrating data from different sources?

Before diving into integration, it is crucial to conduct a comprehensive analysis of your business needs. This involves evaluating the current systems and processes in place, as well as identifying any gaps or inefficiencies that need to be addressed. By taking the time to thoroughly understand the specific needs of your business, you can ensure that the integration process is tailored to meet those requirements effectively. This analysis may involve gathering input from various departments within your organization, consulting with key stakeholders, and conducting thorough research on potential integration solutions. By investing time and resources into this analysis phase, you can set yourself up for success in implementing.

We have a similar use-case of integrating data from different sources, we came up with a detailed architecture to ensure security, integrity, consistency, and usability of the data. Find below key considerations for effectively integrating data from various sources: Define Objectives and Requirements Identify Data Requirements Assess and Understand the Data Sources: Create an inventory Evaluate Data Quality: Assess quality of data from each source, considering accuracy, completeness, timeliness, relevance and file formats. Design the Integration Architecture: ETL vs ELT, Batch vs realtime, SLA's, KPI's etc to select Integration Tools and Technologies Implement Data Integration Processes Establish Data Governance and Quality Controls

In my experience it is critical to determine the questions you want the data to answer and the timeliness of the data needed to answer them. This then drives clarity on batch vs. streaming, ability to verify the schema definition(s) support the ability to answer the questions and drive changes as needed based on if any gaps are identified.

Integrate only the source you're going to need or use for analytical insights. Leveraging a data lake/landing zone the task of adding new sources in their "raw" format is quite fast and agile. The challenge comes after this ingestion, the modelling of several source systems can be time consuming and complex, if you're planning to do an Enterprise Data Warehouse choose wisely what needs to be modelled and try to make your data model has open as possible. Also, try leveraging industry data models because they can speed-up this task and they can provide foundations for entities and domains.

This should always start with the underlying business goal. Why do we even need to integrate this data at all? It better be because there some underlying business value otherwise we are just playing around with increasingly awesome tooling. At an enterprise level, there may be hundreds or thousands of business goals involved making it impossible to fully document or quantify. This is why human relationships between the end data "users" and the data architecture team are more important than the architecture itself. These relationships can be fostered by actively managed data & analytics communities where information is openly shared between the two teams.

When it comes to managing data sources within an organization, having a clear integration strategy is crucial. Without a well-thought-out plan for consolidating data from various sources, businesses risk facing inefficiencies, errors, and inaccuracies in their decision-making processes. An integration strategy helps ensure that all data sources are properly connected and synchronized, allowing for a more holistic view of the organization's operations. Developing a data integration strategy is crucial for organizations looking to manage and utilize their data effectively. Without a well-considered data integration strategy, organizations risk making decisions based on inaccurate or incomplete data, which can have serious implications.

For ETL processes, you may be tempted to lean on a reputable, general solution using Tableau, Power BI, Alteryx or similar. For complex and unforeseen challenges, may I suggest that you keep your options for code as open as possible? These platforms may allow for Python or R scripting to extend your flexibility. But some cases might do best with a standalone routine (Perl, Python, PHP) and here's an example... you want to merge two sources, but one has records summarizing a 24-hr period while the other goes down to 5-minute granularity. You want to use the granular visibility to help break down that 24-hour period into a more useful hourly snapshot. You do this statistically with a simple script without the overhead of a canned platform.

Major challenges of the Data Integration are 3 V’s related to the Data Acquisition. Verity of the data and formats needed common entry point to convert to standard format. Velocity of the data coming from source also needs many channels and technologies which can be automated at this stage. Finally large Volume of the data and schema can be compressed and stored for optimal usage. Protect your data platform from extra terrestrial activities!

I have seen many ETL project which plan thorughly and decided existing tech stacks.But within few months or few years whole architecture change due to business requirment improvement or changes.Such as previously they request data or information daily or hourly but now they need near real time ,More feature adaptation (combine different new data source,types etc) .So in my experience it is very difficult to decide fixed long data journey for the organisation.Best thing is adaptation base on new technologies,requirements et and fast time to market

Integrating diverse data sources in data architecture demands a well-crafted strategy. Start with a thorough assessment of data sources and business goals. Map and transform data to ensure consistency. Select suitable integration technologies, focusing on robust ETL tools or iPaaS solutions. Implement and rigorously test the integration, ensuring data integrity. Continuously monitor and adjust the system to handle new data challenges. Key pillars include data mapping, transformation, technology selection, and ongoing system maintenance, all aimed at creating a unified data environment for informed decision-making.

Maintaining keys and referential integrity is very challenging when combining complex data from different sources. I have found that utilizing a graph database to hold explicit connections is generally better than relational databases. The example mentioned about customer_id vs client_number is very typical, and generally leads to poor designs in which the only place where the correct mappings are known are in the queries, rather than in the schema itself.

Data mapping is an iterative process, and it's essential to continuously refine and optimize the mapping rules based on feedback, changes in data sources, or evolving business needs. Regularly review and update the mapping documentation to maintain accuracy and alignment with the data architecture.

Absolutely, data mapping ensures data consistency after integration. It defines how corresponding fields from various sources align (e.g., 'customer_id' vs. 'client_number'). Modern techniques enhance this process: Fuzzy Matching: Goes beyond exact string comparisons, identifying probable matches with slight field name/format variations. Machine Learning: Algorithms learn complex data patterns, uncovering subtle matches manual methods might miss. By leveraging these alongside traditional mapping, we create a robust data reconciliation process, minimizing inconsistencies and guaranteeing a high-quality, unified data set for analytics.

Mapping all data sources relevant to business problem , visualising the schemas in any tool can help in achieving clarity and identifying gaps. There are several free tools available to help in effective exploration. Some data warehouses may have those features built in. DBSchema is one generic tool with a community edition which can be used to explore the data tables in any datawarehouse. It can accelerate the exploration process and enable effective mapping of the fields. However, some domain knowledge is always good to have, and consulting with a domain expert can improve the results.

Data Integration Tools: Utilize data integration tools and platforms that support data extraction, transformation, and loading (ETL). These tools help automate the process and provide features for data cleansing, transformation, and validation. Data Modeling: Develop a unified data model that accommodates data from all sources. This may involve creating a data schema or data model that standardizes terminology, formats, and structures.

When integrating data from multiple sources, comparing consistency of master data values and definitions is very important because they may have good quality and validity for each source but incompatible for integration. Master data management is crucial for integration data from many sources. Another important data consistency aspect is time consistency - data subsets from each source may not get well integrated if extracted at different time points.

As previously mentioned, establishing a second checkpoint with analysis frameworks and report outputs derived from properly designed systems will instill confidence in data stakeholders. This approach provides an additional layer of validation, ensuring the accuracy and consistency of the integrated data. Furthermore, regular data audits and reviews can be implemented to continuously monitor and assess the quality and integrity of the data. This proactive approach helps identify and address any potential issues early on, preventing them from impacting the reliability and usability of the data.

Data quality frameworks should be built on top of data loads to ensure data quality is maintained and not reported after by business or any downstream systems. We should also build some pre checks on the data source extraction and setup reject layer for failing data constraints.

Implement measures to ensure the quality & consistency of the integrated data. This may involve data cleansing, deduplication, standardization & validation procedures to improve data accuracy & reliability.

Implement data quality checks and validation processes to identify and address errors, inconsistencies, and missing data. Establish data quality metrics and thresholds to measure the effectiveness of the integration process. Implement data cleansing and enrichment techniques to improve the quality of integrated data. Monitor data quality continuously and implement corrective actions as needed.

Envision an architecture diagram that radiates clarity and transparency.... Forge a security tunnel that binds tightly around the integration, safeguarding data with unwavering vigilance on both ends. Embark on a journey of meticulous scrutiny, reviewing the very essence of data structure, and carve pathways where access is delineated with absolute precision - be it the power to read or inscribe. Illuminate the path forward with a beacon of resilience, where contingencies shimmer with unmistakable clarity, ensuring that even amidst adversity, the journey continues unimpeded. Embrace change with a symphony of preparedness, where change management orchestrates a harmonious transition, guiding every transformation with grace and foresight.

Evaluate how data source demand security measures to data team, for example: > REST API or Webhook: Whitelist IP, functionality control or security channel. > Databases access: Whitelist IP, user management, permission control or security channel. Ask for what security measures you must meet to integrate it in your data architecture.

Data security is critical when integrating data from diverse sources. This includes strict access controls, encrypting data in transit/at rest, and regular system audits. Access controls ensure only authorized personnel access sensitive data. Encryption safeguards data confidentiality during transmission and storage. Regular audits detect vulnerabilities and unauthorized access attempts. Data masking and anonymization techniques further protect sensitive information. Secure APIs and connections, like HTTPS and VPNs, ensure safe data exchange. Data integrity checks prevent tampering or corruption. Adherence to regulations such as GDPR and HIPAA is vital, ensuring legal compliance and reducing risks.

Define data governance policies & procedures to manage the integrated data effectively. This includes establishing roles & responsibilities, defining data ownership, enforcing data security & privacy measures & implementing data quality controls.

When building any system data security is sometimes overlooked but it is very crucial . We need to build entitlements to ensure right data is presented to right stakeholders. In some cases such as row level security , setting up LDAP groups on db objects etc are even the smallest things which enables data security

To have good monitoring it is first necessary to have good base metrics, for example, you have the publication of all sales for the day. How long does it need to be available? near real time, online, every 3 hours or another metric? What are the types of consumption? file, report in a visualization tool, etc. With this understanding we can create minimum quality metrics. Data volume, variation in values, main data sources, etc. Based on this, it is possible to create monitoring metrics and consequently alerts to avoid problems with decisions based on incorrect data.

Set up some sort of automated monitoring with the feedback from the automated monitoring goes to an active and alert group of support personnel. Nothing is more annoying than trying to pull outdated data from a system whose data pipelines stopped working at some unknown point in the past and nobody knows how to get it started again.

In addition to the above outlined steps (which are good), its also important to establish a stakeholder engagement & change management plan. Keep involvemt of key end users. Get their input. Also ensure there’s top-down sponsorship for the changes being made. Must have executive level buy-in.

To monitor performance in data integration, track key metrics like processing time, throughput, and error rates. Use monitoring tools to detect bottlenecks, anomalies, and system failures. Set up alerts for critical issues and conduct regular performance tests to identify areas for improvement. Analyze historical data to spot trends and optimize processes. Implement scalability measures to handle increasing data volumes efficiently. Continuously review and update performance monitoring strategies to ensure optimal system performance.

In virtually any modern environment of meaningful sophistication, regardless of it being a greenfield or legacy, integrating multiple data sources is a fact of life. As such, I would suggest that developing a suite of design patterns to ingest various data sources consistently and in a timely fashion may be useful to be part-and-parcel of a modern data architecture.

First thing First, Identify the various data sources available within your organization or external sources relevant to your business needs. Understand the structure, format, and quality of data in each source. Second Are you sure your data is clean ? Of course it can't be , So finalize and apply controls as early as you can , preferably just after ingestion into the Lake. You can relax and enjoy quality data. Establish a framework for data transformation and enrichment. It's very often, the Monitoring and maintenance requirement of the DATA(Not Jobs) are overseen, Do analyze, brainstorm and implement. Data Governance and Security is Must - Stay 100% Compliant all the time ( Come up with some Process). Finally DR,Retention, Documentation,

With an ever increasing amount of data and an equally decreasing quality level of that data, I would suggest to think before one would design an integration architecture. What are the sources, targets, what are the data quality rules (select, transform, deliver) and where will any errors be followed up? Additionally - in a significant number of cases, treating masterdata as such will likely increase data consistency including quality. - ensure you know what data you have and why & when it needs to be available in/between sources; - proactively manage the quality of the data; - consider mastering relevant data before integrating as-is; - use different design patterns based on need and type of data. Hope it helps!

When integrating data from different sources the following subect area is key to better DQ: Master Data Management is a critical data management technique to ensure that same entities are identified and clubbed together to eliminate duplication in data mapping. In a CRM system it is very crucial to merge the same customer from different sources to ensure higher data quality when dealing with data related to that customer.

One of the most important prerequisites for any data integration is data profiling, this will build data insights and drive data mapping or data quality rules to measure data accuracy and linkage between systems.