How can you integrate big data with other systems for a holistic view?

Some possible ways to integrate big data with other systems are: - Use APIs to connect big data platforms with existing applications and databases - Implement data pipelines to ingest, transform, and load big data into a data warehouse or a data lake - Apply data governance and quality standards to ensure consistency and accuracy of big data across systems - Leverage cloud services and tools to enable scalability, security, and accessibility of big data

There are two main types of data pipelines, which are batch processing and streaming data. Batch processing loads “batches” of data into a repository during set time intervals, which are typically scheduled during off-peak business hours. Streaming data, on the other hand, involves processing data in real-time as it arrives. Important to mention is that before data flows into a data repository, it usually undergoes some data processing. This includes data transformations, such as filtering, masking, and aggregations, which ensure appropriate data integration and standardization. Furthermore it’s important to ensure the quality and accuracy of your data. This involves checking if your data is complete, consistent, reliable, and valid.

The thing about APIs is that their consumers generally do not like Big Data. API consumes expect speed. A 2ms response time is responsible, and extremely tough to achieve if you attempt to query a Big Data platform in response to an API request. Some will say, "but Google uses APIs to externalize Big Query data, and you would be right, but these APIs are not designed for real-time. To maintain performance, you need to: 1) Cache the data in an in-memory cache like Redis 2) Pre-calculate aggregates and store in the cache Avoid implementing APIs that support ad-hoc queries at all costs.

Here are a few more points to consider about data federation: 1.One of the key benefits of data federation is that it allows real-time access to data. Since the data remains at the source, integration happens in real time as data consumers apply one query across multiple sources. 2.Data federation is sometimes referred to as data virtualization. It involves the creation of a virtual database that maps an enterprise’s many different sources and makes them accessible through a single interface. 3. Data federation comes with its own set of challenges. These include dealing with proprietary query formulations, proprietary extensions, and semantic variations across different data sources.

In my experience, data federation is an excellent approach for integrating big data sources to gain a holistic view. To enable a unified view, we implemented a data federation layer using Presto. With data federation, we created a virtual database that provided integrated access to data across our data lake, relational databases and even SaaS applications. I do recognize data federation introduces complexities around proprietary SQL dialects, semantic harmonization and latency across retrieval. However, the benefits outweigh challenges. With thoughtful metadata management and query optimization, data federation delivers tremendous flexibility to integrate siloed big data and serves as a scalable alternative.

I suggest Microsoft Dataverse for data synchronization: 1.It has a number of specialized messages for specific operations that update records. 2. In enterprise deployments, it’s common for data from external systems to be loaded into DV so that it can be presented to users. These external systems often can’t be extended to store the DV record GUID PK identifiers to synchronize data efficiently between the systems. A common solution is to add a custom column to a table in DV that can be used to store the identifier of the related record in the external system. 3. The change tracking feature in MDV provides a way to keep the data synchronized in an efficient manner by detecting what data has changed since extracted or last synchronized.

Integrating big data with other systems starts with having a strong data governance strategy in place. We were bringing in clickstream data from our website along with sales and customer data from our CRM system. To get a holistic view, we needed to connect and match data across these systems. Based on personal experience, I've found that clearly defining data ownership, data quality standards, metadata, and access controls through data governance policies is crucial. Overall, thoughtful data governance lets you integrate big data sources while maintaining accuracy, security and compliance.

Lack of data governance leads to various issues such as low data quality , security and privacy. Data governance should become mandatory at every organization and people working with data should have knowledge about governance. All the new technologies and tools we have make this task easy. But the first step is to acknowledge that governance is a necessity

A Data Mesh offers an alternative method to surface data and big data, while conforming to a company wide governance policy, without maintain many data copies and providing a standard way to secure. Companies like Databricks offer a Data Mesh platform, which adheres to Open Standards. Theirs is called Unity Catalog & Delta Sharing. Together they can be used to implement a Data Mesh. A mesh provides for 4 principles, 1) domain-driven data ownership 2) data as a product 3) self service 4) federated governance A mesh is a commitment, but offers a strategy that simplifies data access, while maintaining governance and security