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Laying the Foundation for Tomorrow with a Modern Data Architecture

Modern data architecture can solve many business problems, streamline your value chain and provide a central data repository for your internal team, partners, and various stakeholders. But still, there are some business owners who have not given a thought about it. Before they consider modern data architecture, they should understand its evolution.

Evolution of Data Architecture

Stage 1:  The Transaction Processing Database

This is the stage where databases were designed for doing transaction processing. They were good at doing that and served the business’s many needs, but they were not designed to do the analytics. These types of databases were available in the early 1970s. Databases were slow, and storage was costly for serving basic business intelligence (BI) and analytical need. Complex BI tools were necessary and skilled personals were required to carry out those tasks.

These tools were not built for end users, though. Data experts were needed to satisfy user reporting requirements. One benefit of this was data governance and reporting accuracy were stellar because only those who knew most about the data were skilled enough to produce the reports. But soon the requirements for reports grew in size and number, so the ability to provide reports in a timely manner was hampered, creating data bottlenecks.

Stage 2:  Database with Self-service BI tools

The data bottlenecks were answered in this stage with departmental data silos and tools designed to work with them. Reporting requirements were met quickly with self-service tools, but data governance took a backseat, which leads to data chaos. These happened where people from different departments would produce reports for the same business metric but have significantly different results because there was no standard procedure for report generation. There was no standardization in defining metrics, also, which lead to an altogether new outcome, which was more time was spent arguing about the authenticity of data than acting on it.

Then organizations needed something that is based on highly governed data and provides the agility of the self-service reporting silos and the accuracy of the reports produced by data experts. This was a new set of techniques that were not only limited by slow database performance or expensive storage. A new computing paradigm was born out of necessity from companies like Yahoo, Google, Facebook, and LinkedIn, whose main asset was data.

They also needed to quickly process and derive value from those incredible volumes of data. New technologies like Hadoop and Spark massively parallel processing databases based on concepts like commodity hardware and elastic resource allocation were built with high-speed analytics in mind which changed the landscape and this led to the third wave of the modern data architecture.

Stage 3: Data Platform Services

The two previous stages were characterized almost entirely by the need to work around existing technology and cost limitations. This new stage required a new way of thinking.

Without the technical and economic limitations that have been imposed on data teams, organizations shifted from report creators to insight generators, educators, and enablers. Data silos could be eliminated to provide users with a comprehensive view of what’s happening and how it all interrelates rather than having to figure out what data can be ignored, reducing storage and processing time. Businesses can now focus on identifying all of the ignored and forgotten data sources that add real value.

Then users can also maximize that value by allowing them to look outside their walls for ways that can help users make better decisions about whatever impacts a business. In addition, they can look for new ways to enable not just their internal business users, but customers, partners, and suppliers to access data that makes them more efficient and effective.

For this to happen, a common language and set of metrics, plus a data dictionary that enables users to ask and answer their own questions allow for data governance en masse. Users can gain a greater understanding of data and how to leverage it. They can also easily access, understand and generate real business value. Now instead of working for one report for one person, experts can just create a reusable model that can be shared with everyone.

Modern data architecture is defined not so much by a specific technology stack but rather by the organizational impact that it enables. Organizations like Looker have developed a data platform service that has an interesting take on situations.

Source: Looker

For example, here is the Looker view of modern data architecture. At the bottom of the diagram, data has been stored in lots of different places. It might have SAP applications, Salesforce or Zendesk, Data, and transactional databases, ERP, and Web analytics tools. That used to be what had to be done to extract transform and then load that data into a warehouse. That transformation step was usually complicated and difficult, so a lot of logic would be baked into the transformation, making it very inflexible.

But because of new databases, there is no need to pre-transform data anymore make this new service plug and play. Tools like Looker sit on top of the database, the platform contains data models, which provide the ability to govern transformation in a flexible and agile way. Once analysts have created the model, anyone in the organization can use it and answer their own questions.

Now let us focus on those technological advancements that helped in making a truly modern data architecture.

Technological Advancements

Cloud Migration and Multi-cloud strategy

According to McKinsey Global Institute, “cloud is potentially the most revolutionary catalyst of a fundamentally new approach to data-architecture since it provides businesses a way to quickly scale up AI resources and capabilities to a competitive advantage.” Cloud migration is the process of moving existing data processes from an on-premise facility to cloud base environment. With server-less data platforms like Amazon S3 and Google BigQuery, organizations can build and operate data-centric applications with infinite scale without worrying about Installation, configuration solutions, or managing workloads. Containerized data solutions using Kubernetes enable companies to detach and automate the deployment of extra computational power and storage systems whenever needed.

Every cloud provider has been offering services with unique propositions. Some cloud providers are better with transaction handling, some are better at managing subscription-based services, and some are better at managing analytical services, so choosing the right cloud partner with the right set of services is critical for organizational success and can save lots of time and money.

Many companies are struggling to manage these services. That’s where platforms like Google Anthos come into the picture. Google Anthos is a multi-cloud infrastructure management platform that can handle the deployment and management of containerized services for any cloud platform that an organization is using.

Artificial Intelligence and Machine Learning in Data Engineering and Operations

A well-set up data pipeline is a work of art because it seamlessly connects multiple datasets to a business intelligence tool to allow clients, internal users, and stakeholders to perform complex analyses. But according to Sisense, a business analytics software company, the data preparation phase of the whole phase has its own issues and complex challenges. It is a creative process and it is necessary, but saving and automating the repetitive usage of the logic every time we want to deploy something new into the system is a challenge. Today with the use of artificial intelligence (AI) and machine learning, it is possible to make the data preparation process more efficient for BI platforms to use it at a much faster rate.

AI can help in data engineering in a few ways. First, through its systems, it can apply simple rulesets to help standardize the data. Secondly, AI can recommend a data model structure, including providing joins for columns and it can create dimensions also, Finally, AI can help in data ingestion and can save a lot of time.

Data operations are the new agile operational technique to emerge from the mutual knowledge of IT and big data practitioners. It focuses on the implementation of data management practices and processes that increase the speed and accuracy of analytics, including data access, quality control, automation, integration, and, eventually, model deployment and management.

Source: Vmblog

Source: Bisinfotech With the agenda of delivering the technology-enabled solutions to its customers which needs to be thrived in today’s […]

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