We live in a new era of data-centered enterprise management. There are various types of modern data coming through different sources, in increasing volumes, and from different locations like never before. We now live in an era of emerging solutions which helps enterprises to meet the challenges in data integration effectively. As we can see, data now is more complex than ever and is largely distributed. So, if we want to take advantage of these data, we must connect the emerging data with modern means of data integration.
While there are a few technologies dominating in this field, data architectures still remain so dynamic. The open-source vendors and innovators continue to make new data lakes, cloud, and streaming options for the enterprise data architectures. With business requirements are also evolving day by day, placement of solid and strategic solutions also become difficult in terms of data integration. So, there is no surprise that the organizations keep on thinking of changing their choices in terms of data platforms and integration methods. Effective management of data now requires application of consistent principles and advanced set of option.
Major data integration use cases
In order to understand and handle the changing nature of data, let us consider some of the most commonly occurring data use cases and the modes to address each. Most of the projects in terms of data integration may come under any of these use cases.
Data lake ingestion – The data lakesbased on S3 or HDFS are becoming more powerful by complementing to the traditional data warehouses as these can process really huge volumes of different data types.
Cloud migration – Cloud is more acceptable now for the cost savings it offers as well as the elasticity of resources and high security. Cloud is also a reliable platform for analytics.
Database transaction streaming – All enterprises need to capture the perishable data values and stream the business records in real-time. Mighty analytics engines make it possible. In addition to it, it is also possible to send incremental updates which will eliminate the overhead of batch loads which may disrupt production.
Data extraction / loading from production sources – Different types ofoperational datalike supply chain, revenue, and other types of operational data from various production systems like Sales Cloud, SAP, Mainframe, and Oracle etc. hold a heap of analytical values. This is more so when the analysis is being done by the external platforms where internal data tend to get mixed with external data as social media trends and web reach etc.
Guiding principles for changing data integration environments
The modern-day organizations are making more strategic choices about their data integration methods based on the available information at different point in times. In order to navigate through these complex and continuously changing options, these companies tend to adopt different guiding principles as below. For database related tasks, you can rely on services offered by reliable providers like RemoteDBA.com.
Testing small workloads and datasets
You may not know how good Hive or Kudu may support the queries while you run it. Once it is done, you may be testing the new workloads on various platforms before migrating by avoiding any sunk costs.
Maintain the flexibility of platforms with data integration
The trial-and-error approach may lead naturally to the need for flexibility in data integration process. The investment into constant data integration surely pays as it can dynamically add and remove the end points.
Reducing developer dependency
With the rise in demand for data Integration and with the frequent need for a change, there are risks of overburdening the ETL programmers and also creating bottlenecks for them. The more automation enterprises brought into this process, the more DBAs and architects will be empowered to integrate data quickly without the need for programmers’ involvement.
Considering multi-staged pipelines
As effective data lakes become with some locks to transform data into various sequential stages, more beneficial it will prepare data for analytics. If needed, you can also rewind the status to an earlier stage for changing the course or to correct the errors.
Keep your data warehouse
There are many organizations which start to treat data lakes as transformational by preparing the data sets to traditional data warehouses to do analysis in a structured way. Standard ACID-compliant data structures always tend to yield highly accurate analytical results.
Principles for cost-effective analytics
Taking data processing to where data is
There is usually too much of data to move each time when there is a need to blend data. You may just place the agents to where the data lives for local processing. Instructions needed to be carefully coordinated and the work has to be done on the host platform before the data is moved. By taking the processing to where data lives, you may eliminate any bottleneck of ETL server and can also decrease data movement across the networks.
Leveraging all platforms on what they are meant to do well
If you have invested heavily on setting up a mighty database, then every such platform may have a set of tools to handle different functions associated with it. Data integration will let you to call out all those native functions locally and process those within the platform itself by distributed the workloads in an effective way. By leveraging the features of these existing platforms, you can optimize the performance of your database.
Manage all data and business logic centrally
The modern-day platforms can be centrally managed with all the data logic and business rules integrated to a single studio. You can do it in an architecture where the central design studio can separate the tasks from local processing agents with the use of native functions. So, with this, you do not have to waste your time and effort for redesigning the flows.
With these cost-effective data management and integration approaches, you do not have to waste your time to repeat the same tasks again. Having all the data management done centrally, you can keep all business rules and in a metadata repository. With this, while any changes need to be made to the data sets, you can make the changes quicker and easier with the existing logic and rules.