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The Rise of the Data Reliability Engineer

With each day, enterprises increasingly rely on data to make decisions. This is true regardless of their industry: finance, media, retail, logistics, etc. Yet, the solutions that provide data to dashboards and ML models continue to grow in complexity. This is due to several reasons, including:

This need to run complex data pipelines with minimum rates of error in such modern environments has led to the rise of a new role: the Data Reliability Engineer. Data Reliability Engineering (DRE) addresses data quality and availability problems. Comprising practices from data engineering to system operations, DRE is emerging as its own field within the broader data domain.

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7 Reasons Why Companies Should Apply DevOps and CI/CD Practices to Their Data Pipelines

Agile experimentation is the new standard in the software development landscape. Organizations aim for releasing the best version of their products as quickly as possible. DevOps and principles of continuous integration and continuous delivery/deployment (CI/CD) prepare them for a quick software release while maintaining security, quality, and compliance. 

DevOps and CI/CD practices facilitate agile software development. Recently, they have embraced transformative technologies like AI to remove the barrier between development (Dev) and operations (Ops), accelerating deployment cycles and software delivery. 

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Why Lambda Architecture in Big Data Processing?

Due to the exponential growth of digitalization, the entire globe is creating a minimum of 2.5 quintillion (2500000000000 Million) bytes of data every day and that we can denote as Big Data. Data generation is happening from everywhere starting from social media sites, various sensors, satellites, purchase transactions, Mobile, GPS signals, and much more. With the advancement of technology, there is no sign of the slowing down of data generation, instead, it will grow in a massive volume. All the major organizations, retailers, different vertical companies, and enterprise products have started focusing on leveraging big data technologies to produce actionable insights, business expansion, growth, etc. 

Overview

Lambda Architecture is an excellent design framework for the huge volume of data processing using both streaming as well as batch processing methods.  The streaming processing method stands for analyzing the data on the fly when it is in motion without persisting on storage area whereas the batch processing method is applied when data already in rest, means persisted in storage area like databases, data warehousing systems, etc. Lambda Architecture can be utilized effectively to balance latency, throughput, scaling, and fault-tolerance to achieve comprehensive and accurate views from the batch and real-time stream processing simultaneously.

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Best Practices for Data Pipeline Error Handling in Apache NiFi

According to a McKinsey report, ”the best analytics are worth nothing with bad data”. We as data engineers and developers know this simply as "garbage in, garbage out". Today, with the success of the cloud, data sources are many and varied. Data pipelines help us to consolidate data from these different sources and work on it. However, we must ensure that the data used is of good quality. As data engineers, we mold data into the right shape, size, and type with high attention to detail. 

Fortunately, we have tools such as Apache NiFi, which allow us to design and manage our data pipelines, reducing the amount of custom programming and increasing overall efficiency. Yet, when it comes to creating them, a key and often neglected aspect is minimizing potential errors.

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Why a Snowflake Computing Warehouse Should Be Part of Your Next Data Platform

The traction for serverless services, including data warehouses, has gained momentum over the past couple of years for big data and small data alike. Scalable performance, along with removing set up or management of infrastructure, has proven attractive. Also, a model of just paying for “run-time” resources is equally as attractive.

Why Snowflake Warehouse?

When we find products that embrace zero data management, data warehouse-as-a-service, we are in. This is why we have taken a closer look at Snowflake Computing. Building on this industry serverless trend is the data warehouse offering from Snowflake Computing. Here is how Snowflake Computing describes their product: