February 2022 Summaries

In the era of data-driven business, companies are leveraging operational data in mission-critical ways to transform their operations and disrupt markets. The speed of business and the rate at which data is created have accelerated, making real-time data access essential for maintaining pace. Data observability platforms provide businesses with the ability to track, manage, and optimize data usage and costs in real time, ensuring the health of all-important data pipelines. Value engineering and data cost optimization are crucial practices for modern data operations, as they help maximize performance, minimize downtime, streamline infrastructure, and create a culture of accountability and data reuse. Data observability platforms like Acceldata enable companies to achieve these goals by providing real-time visibility, intelligence, and control over their data assets.

The text discusses the shift from on-premises to cloud-based data management solutions due to issues with traditional systems like Cloudera and Hadoop. It highlights how cloud platforms like Snowflake, DataProc, and AWS EMR have made it easier for users to adopt innovative approaches such as data meshes and marketplaces. The text emphasizes that a successful data migration involves more than just moving data from on-premises to the cloud; it requires assessing inventory of jobs and processes, creating an inventory of data assets, understanding target cloud platform architecture, and re-architecting and refactoring data layouts and transformation flows. The text also explores how Acceldata helps in migrating data from Hadoop technologies to Snowflake through various phases like Proof of Concept, Preparation, Data Migration, Consumption, Monitoring, and Optimization.

Bad customer data costs companies six percent of their total sales, according to a UK Royal Mail survey. The overall cost of poor data quality for U.S. businesses is estimated at $3.1 trillion per year. As companies become more data-driven, ensuring reliable and high-quality data becomes critical. Data observability platforms offer a proactive approach to solving data quality issues by continuously monitoring and profiling data in motion, reducing the complexity and cost of ensuring data reliability. These platforms can help businesses meet SLAs, reduce cloud fees, and allow data engineers to focus on more strategic tasks.

Poor data quality costs enterprises an average of $15 million annually, and improving it is not a one-time activity. To achieve better business outcomes, companies need to incorporate data quality best practices into their operations using a data observability solution. This allows data teams to understand their data at a granular level, optimize their data supply chains, scale their data operations, and continuously deliver reliable data. Data observability provides a unified view of data processing and pipelines throughout the data lifecycle, automatically detecting data drift and anomalies from large sets of unstructured data. By aligning data operations with business needs, monitoring workloads, and predicting future capacity requirements, enterprises can improve performance, lower costs, and achieve a 1,000x return on their data observability investment.

Minimalism is gaining popularity as simplicity becomes a valuable commodity in an increasingly complex world. In the business technology realm, IT administrators are looking for ways to cut complexity in their data environments. Many are turning to single, neutral multi-platform cloud solutions to reduce the cost and labor of administration. The three reasons why choosing one overall data management tool rather than multiple ones is better include lower licensing and subscription fees, easier learning which saves time and money, and a centralized view over the entire infrastructure. Data observability platforms provide extensive visibility into all layers of a distributed data infrastructure, helping to solve current problems and prevent future ones.

The era of Hadoop is coming to an end as businesses move towards more cost-effective, faster, and easier-to-manage alternatives for big data analytics. Many companies are now considering migration paths such as rebuilding their on-premises Hadoop clusters in the public cloud or migrating to a new on-premises or hybrid cloud alternative. Another option is to move to a modern cloud-native data warehouse, which promises real-time performance and automatic scalability. However, any migration involving large amounts of data should be carefully planned and tested to avoid potential issues. Deploying a multi-dimensional data observability platform like Acceldata can provide powerful performance management features for Hadoop and other big data environments, making the migration process smoother and more efficient.

The Acceldata Engineering team developed a utility called Kapxy to identify Kafka Producer-Topic-Consumer relationship metrics. They discovered that the required information was not available in APIs or JMX metrics, and after deep diving into Kafka's internal implementation documentation and network protocols, they found that each Produce request had a "client_id" field (Producer ID) and a "[topic_data]" field containing Topic names. By extracting these metrics from the network packets sent to Kafka broker using their newly developed Kapxy utility and JMX metrics already collected, they were able to plot a chart showing the relationship between Kafka Producer-Topic-Consumer components in Acceldata Pulse's Kafka dashboard.

Data teams struggle with cleaning and validating incoming data streams using ETL validation scripts due to their costliness, time-consuming nature, and difficulty in scaling. This issue is expected to worsen as enterprises face a projected 3x increase in data growth over the next five years. To maintain control over data operations and ensure effective cleansing and validation of data, an automated approach is necessary. Automated data observability solutions like Acceldata Data Observability Platform can automatically clean and validate incoming data pipelines in real-time, enabling enterprises to make data-driven decisions based on the most current and accurate data available. Manual ETL validation scripts have limitations such as being unable to handle real-time data streams, causing delays in analyzing real-time data, higher data infrastructure costs, and constraints on resources and data quality problems. Automatically validating data streams in real-time can help enterprises avoid paying for incomplete or incorrect records, reduce their overall cost of handling data, and allow data teams to focus more on innovation rather than mundane tasks.

Acceldata has released version 1.2.1 of its data quality and reliability platform, Torch. This update introduces new features such as a partition-based incremental strategy sub-strategy, the ability to specify result locations at the asset level, support for warning thresholds in policy creation, tagging policies, displaying user information for policies, selecting databases/schemas while creating data source connections, and a horizontal scroll bar for sample data. These enhancements aim to improve data pipeline management across ingestion and consumption stages.

The text discusses how modern data engineering teams face repetitive, tedious tasks due to the massive data explosion resulting from digital transformation. These teams are responsible for ensuring the reliability and optimization of data supply chains but have been overwhelmed by the COVID-19 pandemic, leading to burnout and resignations. Automation is seen as a solution to these challenges, with machine learning used to predict issues and improve accuracy in data management. Acceldata provides multidimensional data observability for platforms, data, and pipelines, automating data reliability across hybrid data lakes, warehouses, and streams. The text also presents a specific use case of Compute Observability for Snowflake, highlighting how Acceldata's automation can address various requirements for effective Snowflake management.