Sunday, September 20, 2020

Data Science - A Modern data lakes

A modern data lake enables organizations to efficiently store, manage, access, and generate value out of data stored in both on premise storage infrastructures as well as in the cloud, allowing organizations to apply next-generation data analytics and ML technologies to generate value from this data. The cost of bad data quality can be counted in lost opportunities, bad decisions, and the time it takes to hunt down, cleanse, and correct bad errors. Collaborative data management, and the tools to correct errors at the point of origin, are the clear ways to ensure data quality for everyone who needs it.

The old data lake terminology supposes to have many challenges where value of the data is not realized such as —

  1. They lead to multiple copies of raw, transformed and structured data has been created and no single source of truth
  2. Data silos from traditional data warehouse not handling unstructured data, additional systems needed
  3. They are built primarily to offer an expensive storage. So, analytics performances slowly and they have limited though put for queries, concurrent users
  4. They are complex and costly, requiring significant tuning and configuration across multiple products
  5. Non-SQL use cases require new copies of data for data science and machine learning
  6. They have limited security and ungoverned capabilities

The above challenges usually resolved within Modern data lakes Technologies for handling all structured and unstructured data in a central repository.

Integrated and Extensible Data Pipelines — Cost effective pipelines to progressively refine reliable data through data lake tables. Rely on pipelines scaling reliably and in real time to handle heavy data workloads and extensible data transformations to suit business’s unique needs.

Use built-in smart features to accelerate your Modern data lake. Today, almost everyone has big data, machine learning, and cloud at the top of their IT “to-do” list. The importance of these technologies can’t be overemphasized, as all three are opening up innovation, uncovering opportunities, and optimizing businesses.

Build and run integrated, performance and extensible data pipelines to process all your data, and easily unload the data back into Modern data lake to store the data with efficient data compression.

Self-service for data scientists and ML engineers — With complete, reliable, and secure data available in Modern data lake, your data teams are now ready to run exploratory data science experiments and build production ready machine learning models. Integrated cloud-based tools with Python, Scala, Hive, R, Pyspark and SQL make it easy for teams to share analysis and results.

Exceptional Query Performance —SQL and ML together on modern data lake with a single copy of data. Open data formats ensure data is accessible across all tools and teams, reducing lock-in risk. Enable efficient data exploration, with instant and near-infinite scalability and concurrency.

Secure, Governed Collaboration — Build once, access many times across use cases for a consolidated administration and self-service. Helps to meet governance and security standards for collaborative data preparation, exploration, and analytics no matter where data resides.

Make Data a Team Sport To Take Up Data Challenges — Data quality is often perceived as an individual task of the data engineer. As a matter of fact, nothing could be further from the truth. Data quality is now increasingly becoming a company-wide strategic priority involving professionals from every corner of the business. To succeed, working like a sports team is a way to illustrate the key ingredients needed to overcome any data quality challenge.

As in team sports, you will hardly succeed if you just train and practice alone. You have to practice together to make the team successful. Also, just as in team sports, Business/IT teams require having the right tools, taking the right approach and asking committed people to go beyond their daily tasks to tackle the data quality challenge one step at a time.

It’s all about strengthening your data quality muscles by challenging IT and the rest of the business to work together. For that, you need to proceed with the right model, the right process and the right solution for the right people.

Eliminates old model :Too few people access too little data — The old model was about allowing a few people to access a small amount of data. This model worked for many years to build data warehouses. The model relies on a team of experienced data professionals armed with well-defined methodologies and well-known best practices. They design an enterprise data warehouse, and then they create data marts, so the data can fit to a business domain. Finally, using a business intelligence tool, they define a semantic layer such as a “data catalog” and predefined reports. Only then can the data be consumed for analytics.

Modern Data lakes then came to the rescue as an agile approach for provisioning data. You generally start with a data lab approach targeting a few data-savvy data scientists. Using cloud infrastructure and big data, you can drastically accelerate the data ingestion process with raw data. Using schema on read, data scientists can autonomously turn data into smart data.

This more agile model has multiple advantages over the previous one. It scales across data sources, use cases, and audiences. Raw data can be ingested as it comes with minimal upfront implementation costs, while changes are straightforward to implement

Collaborative & Governed Model — By introducing a Wikipedia-like approach where anyone can potentially collaborate in data curation, there is an opportunity to engage the business in contributing to the process of turning raw data into something that is trusted, documented, and ready to be shared.

By leveraging smart and workflow-driven self-service tools with embedded data quality controls, we can implement a system of trust that scales. IT and other support organizations such as the office of the CDO need to establish the rules and provide an authoritative approach for governance when it is required (for example for compliance, or data privacy.)

Choosing The Right Tools — Data profiling is the process of gauging the character and condition of data stored in various forms across the enterprise — is commonly recognized as a vital first step toward gaining control over organizational data. The right data pipeline tool delivers rich functionality that gives you broad and deep visibility into your organization’s data:

  1. Jump-start your data profiling project with built-in data connectors to easily access a wide range of databases, file types, and applications, all from the same graphical console
  2. Use the Data Explorer to drill down into individual data sources and view specific records
  3. Perform statistical data profiling on your organization’s data, ranging from simple record counts by category, to analyses of specific text or numeric fields, to advanced indexing based on phonetics and sounds
  4. Apply custom business rules to your data to identify records that cross certain thresholds, or that fall inside or outside of defined ranges
  5. Identify data that fails to conform to specified internal standards such as SKU or part number forms, or external reference standards such as email address format or international postal codes
  6. Improve your data with standardization, cleansing and matching. It also allows you to identify non-duplicates or defer to an expert the decision to merge or unmerge potential duplicates
  7. Share quality data without unauthorized exposure. User can selectively share production quality data using on premises or cloud-based applications without exposing Personally Identifiable Information (PII) to unauthorized people

Modern data stewardship — As a critical component of data governance, data stewardship is the process of managing the data life cycle from curation to retirement. With more data-driven projects being launched, “bring your own data” projects by the lines of business, and increased use of data by data professionals in new roles and in departments like marketing and operations, there presents a need to rethink data stewardship.

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