Mastering Data Observability with Datameer

In the world of big data, organizations are constantly dealing with increasingly complex data ecosystems. With a myriad of data sources, tools, and technologies, managing and maintaining data quality, security, and accessibility is no easy task. 

Data observability is a game-changing approach to modern data management that enables businesses to ensure their data is reliable, accurate, and, most importantly, actionable.

In this guide, we’ll explore what data observability is all about, why it matters, and how Datameer helps organizations achieve it.

And don’t worry, we’re keeping things informal, so it’ll feel like a chat with a friend who just happens to know a thing or two about data observability.

Data Observability 101: What It Is and Why It Matters

Data is like the secret sauce that keeps modern companies running. It helps make decisions, sparks new ideas, and keeps things growing. But let’s be real, data can also be a total mess.

It’s always changing and moving through a bunch of data pipelines, from where it comes from to where it’s going, and hopping between different systems and platforms.

 So, how do you make sure your data is on point, on time, and trustworthy? How can you dodge stuff like bad data quality, pipeline disasters, performance roadblocks, and even breaking the rules? How do you make your data operations run smoothly and give your business peeps what they need?

Well, the answer is data observability! It’s a combo of tools and practices that help you keep an eye on your data and data systems so they stay in tip-top shape. 

It’s like an enhanced version of your regular data monitoring, giving you a complete and powerful view of everything going on in your data world. 

It helps you catch and fix data problems before they mess with your biz results and keep your users happy. Plus, it boosts the value, completeness, and quality of your data, so you can make even better decisions with confidence. 

And on top of all that, it helps your data teams work faster and smarter, so they can hit those all-important deadlines.

The Next Big Things in Data Observability Technology and Innovation

Data observability is always changing, thanks to new tech and innovations. Here’s a quick look at some of the biggest trends shaping the future of data observability.

1.AI and ML for data observability

AI and ML help data teams automate and improve their data observability processes. They’re great for tasks like:

• Collecting and correlating data from various sources
• Detecting anomalies or errors
• Alerting teams of issues and suggesting remediation actions
• Analyzing root causes and predicting future behavior
• Recommending best practices for data quality and performance

AI and ML are becoming more accessible as tools like Datameer offer advanced features for data observability.

2. eBPF for data observability

eBPF ( extended Berkeley Packet Filter ) lets users write programs that collect or manipulate data from various events in the Linux kernel. It provides a flexible way to instrument different aspects of systems and applications without changing their source code. Datameer, for example, uses eBPF to provide granular visibility into Snowflake query performance such as CPU usage,memory usage, IO latency etc.

3. Open standards for data observability

Open standards, like OpenTelemetry , promote interoperability among various tools and platforms, helping teams avoid vendor lock-in and reduce complexity. 

OpenTelemetry offers a uniform, vendor-neutral way to instrument different aspects of systems and applications, as well as a common format for exporting telemetry data. For example, Datameer leverages OpenTelemetry to provide seamless integration with various tracing backends, such as Jaeger, Zipkin, etc.

These trends are making data observability more powerful, sophisticated, and accessible than ever before.

Next, we’ve got some handy tips and tricks to level up your data observability game.

The Dos and Don’ts of Data Observability

Data observability is all about tools and practices that help you watch over and improve the health of your data and data systems. It’s super useful, but it’s not gonna magically fix everything. You need a strategic approach and the right mindset. Here’s a quick rundown of do’s and don’ts to help you win at data observability.

Do: Set clear goals and metrics

Before diving into data observability, figure out your goals and metrics. What do you want to achieve, and how will you measure progress and success? Make sure these align with your business goals, so you can focus on the most important aspects of data observability.

Don’t: Try to do it all at once

If you try to observe everything, you’ll end up overwhelmed. Instead, prioritize your data observability needs and focus on the most important data pipelines or metrics. Use a risk-based or value-based approach to figure out which areas to tackle first.

Do: Automate as much as you can

Manual processes in data observability can be slow and error-prone. Use tools that can collect, analyze, and visualize data events in real time, as well as detect anomalies and trigger remediation actions automatically.

Don’t: Forget about the human factor

Remember that data observability is also about the people creating, consuming, or managing the data. Keep their needs and feedback in mind, communicate clearly about your goals and processes, and empower them to access and use the data they need.

Do: Learn from the insights you get

Use data observability insights to figure out the root causes of issues, spot trends, and identify opportunities for improvement. Take action to boost data quality, reliability, and performance.

Don’t: Be satisfied with the status quo

Data observability is an ongoing process. Keep up with changes in your data landscape, explore new tech and innovations, and keep refining your goals, metrics, tools, and practices.

Keep these do’s and don’ts in mind to set yourself up for success with data observability.

The Top 5 Pain Points of Data Pipeline Management

Data pipelines are like the superhighways for your data. They link all your data sources to places it needs to go, like databases, warehouses, lakes, BI tools, and ML models. Along the way, they transform, enhance, and check your data, making sure the right info gets to the right peeps at the right moment.

But, let’s be honest, managing data pipelines is a pain. Data teams run into a ton of obstacles and issues, like:

1. Data quality issues: One of the biggest (and priciest) probs in data pipeline management. Stuff like human mistakes, system errors, and format changes can all cause data quality issues. This can mess with the accuracy, completeness, and consistency of your data, leading to wonky reports, insights, or actions. Even worse, it can break the rules and damage your reputation.
2. Pipeline failures: Another massive issue. Pipeline failures can happen for loads of reasons, like network problems, hardware or software glitches, or dependency issues. They can result in lost or corrupted data, delayed deliveries, or broken downstream effects.
3. Performance bottlenecks: Yet another common headache. Things like limited resources, clunky code, or scalability issues can cause performance bottlenecks. These can slow down your data pipelines, leading to laggy apps, annoyed users, or missed chances and deadlines.
4. Compliance risks: A super serious problem. Compliance risks can come from loads of places, like legal rules, ethical standards, or contracts. They can involve all sorts of data aspects, like security, privacy, or quality. Breaking compliance can land you with fines, lawsuits, or other penalties, and can totally wreck your trust, reputation, or brand.
5. Visibility gaps: One more critical issue. Visibility gaps are when you can’t see what’s going on with your data pipelines or their health. This makes it really tough to figure out what’s happening, why it’s happening, and how it affects your biz. It also makes it crazy hard to spot, diagnose, or fix any of the above problems, and even tougher to make your data better or more innovative.

These are just some of the biggest headaches that data teams deal with every day. They can really drag down your business performance and user experience, and waste a whole lot of time and effort for your data teams.

Now, let’s dive into the next section and see what solutions we’ve got for these problems.

How Datameer Simplifies and Streamlines Data Observability

Datameer is this awesome platform that’s all about data observability. It’s got loads of cool features and capabilities that make it super easy to keep an eye on your data and data systems. With Datameer, you get:

• A full view of all the data pipeline stats and details, like how fast things are happening and how much data’s involved.
• Total visibility into data formats at every stage of the pipeline, from start to finish.
• Automatic detection and alerts for any weird stuff happening in your data pipelines.
• Data quality control with rules, checks, and ways to fix problems.
• Data lineage and impact analysis to help you see how everything’s connected and what changes are happening.

With Datameer, you can:

• Make sure your data gets where it needs to go on time for speedy decisions.
• Boost the value, completeness, and quality of your data for better, more informed decisions.
• Build more trust in your data so your biz can make data-driven moves with confidence.
• Help your data teams work faster and smarter, and hit those all-important deadlines.
• Datameer is made for Snowflake, the top dog in cloud data platforms. It taps into Snowflake’s amazing features to give you a smooth, scalable data observability solution. 

Datameer plays nice with Snowflake’s native tools and APIs, so you can access, transform, and analyze your data right there in Snowflake. Plus, it supports Snowflake’s role-based access control (RBAC) and encryption to keep your data safe and compliant.

Datameer is super user-friendly and easy to learn. You can visually explore, build, and automate data insights using SQL or No Code, all in one place. It also comes with a built-in data catalog, so you can search through your whole data landscape with one-click access to asset metadata and documentation. 

And to help you get started and succeed with data observability, Datameer offers a bunch of templates, samples, tutorials, and support.

Datameer is trusted by data teams across loads of industries and domains. It helps them create a library of trusted data assets for their users and use those assets for things like analytics, reporting, or machine learning.

 Loads of customers have already nailed their goals and tackled their challenges with data observability, thanks to Datameer.

Up next, we’re gonna share some of those success stories from Datameer customers across industries.

Success Stories from Datameer Customers Across Industries

Datameer is  a total hit with data teams in all sorts of industries and domains. It helps them build a library of trusted data assets and use those assets for things like analytics, reporting, or machine learning. Loads of customers have already crushed their goals and tackled their challenges with data observability, thanks to Datameer.

Check out these success stories from Datameer customers in different industries:

1. Reliant Funding: These guys offer alternative financing solutions for small and medium-sized businesses. They picked Datameer to replace Tableau Prep, bring all their data sources together, and automate their data pipelines. With Datameer, they managed to cut their data engineering time by a whopping 500%, create just-in-time analytics for better business flexibility, and let their analysts self-serve their data needs.
2. Vivint: A top smart home company with security, automation, and energy management solutions. They used Datameer to slash the time it takes to turn raw data into actionable insights by 90%. They could bring in and transform data from sources like sensors, cameras, and thermostats, and analyze it in Snowflake. Datameer helped them step up their customer service, cut down on churn, boost revenue, and make their customers happier.
3. Sophos: A big name in cybersecurity, protecting over 400,000 organizations of all sizes. They used Datameer to build a cloud-based data lake as a single source of truth for their data . They could bring in and transform data from sources like CRM, ERP, and web analytics, and analyze it in Snowflake. Datameer helped them improve sales, marketing, customer retention, and product development.
4. i-5O: They provide AI-powered solutions for the insurance industry. They turned to Datameer to set up scalable machine learning processes and analytics in the cloud. They could bring in and transform data from sources like claims, policies, and telematics, and analyze it in Snowflake. Datameer helped them deliver faster, more accurate insights to clients, cut costs and risks, and make their customers’ experience even better.
5. BT Openreach: A division of BT Group, they provide broadband access network services in the UK. They used Datameer to tackle their unique and tricky operational analytics problems. They could bring in and transform data from sources like network devices, customer orders, and engineer visits, and analyze it in Snowflake. Datameer helped them improve network performance, customer service, resource allocation, and revenue generation.

These are just some of the awesome success stories from Datameer customers in different industries. You can find more customer stories on the Datameer website.