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[Merilyn Mardis]

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I joined Virgin Media Business back in September 2014 with the aspirations to one day manage my own sales team. Following three and a half years of individual sales success, hard work and resilience, I was delighted to be successful in my application to be the team manager of the Business Development Team (Desk) at VMB with immediate effect from April 2018.

Although during my time within the sales industry, I have always been an avid fan of LinkedIn, I have not always been the most active of users. However, following my recent promotion into a position that I have feel best accommodates my personality, work ethic and ambitious nature, I thought now would be an appropriate time to utilise the professional networking site more frequently and share my experiences along the way.

After inheriting a team of hardworking and ambitious salespeople, I quickly noticed that due to the nature of the sales industry itself and alongside their desire to hit target, the vast majority of my team were using a transactional sales method. Although this is a successful method to an extent, in my opinion using a transactional approach within a sales environment will always limit the amount of success a salesperson can achieve with their opportunities. Additionally, it does not seem to be the most effective sales method for ensuring high customer retention and referrals. In order to bring about consistent and positive sales figures, both customer retention and referrals are pivotal.

Using my own experience in a far more consultative world (SME) and after selling high value solutions such as leased lines for a number of years, I have implemented a number of minor changes during my first quarter as the team manager to bring about a change in the teams culture, confidence and conversations.

Secondly, installing the belief and confidence into a team to ensure they believe in what they do is also paramount for success. During my time in sales, I have seen salespeople who do not believe in what they are doing and without this, failure is inevitable. One of my first actions since taking control of the team was to install confidence by offering support, guidance and recognition. Confidence in any aspect in life is key but no more so than in sales. Confidence can take months or years to build but seconds to destroy. Therefore, one of my key areas moving into H2 is to ensure regardless of results, feedback is always relevant, detailed and constructive to maintain highest possible confidence level within my team.

My first quarter in management has been very exciting, eye-opening and successful. I am very pleased to achieve over 100% to my target during this time and to have helped people develop onto the next stage of their career.

I am very much looking forward to Q3 and Q4 and working with my team both individually and collectively to ensure they are having the correct conversations, are confident in everything they do and are adhering to the culture I am trying to build.

The significant trends and transitions of the last few decades have created an entirely new tech stack: cloud-based services, horizontal scale, immense amounts of data, and more. We know such significant trends and new aspirations can break existing ways of doing things. For example, the GPU was born of the desire for graphic experiences that CPUs were not delivering. The same could be said for databases; columnar databases are born of the desire for scale, performance, and cost that row-based relational databases are not delivering.

However, there is no one size fits all. Existing Online Transaction Processing (OLTP) may still be a better fit for transaction-oriented processing, but when it comes to Online Analytical Processing (OLAP), columnar databases shine. This is relevant to the series of blogs I am currently writing about Observability (Observability 2025).

First, let's distinguish between critical concepts: in-line and real-time analysis. In-line analysis occurs as streaming data is ingested, providing immediate insights. On the other hand, real-time analysis, as discussed here, refers to exceptionally fast database queries and analytics, enabling quick decision-making. This distinction is crucial as it shapes how we understand and optimize data storage for performance and cost-efficiency across the entire observability pipeline.

In the early days, data processing was often sequential. Entire files were read and processed in bulk, which was suitable for batch processing but inefficient for complex analyses. As the demand for sophisticated data operations grew, so did the need for better data management systems. Enter relational databases, which brought several improvements: Complex Queries, Concurrency Control, Data Integrity, Reduced Redundancy.

While relational databases marked a significant advancement, they often retrieve whole rows of data, which is inefficient for certain types of analysis. Columnar databases, a kind of relational database, address this by:

Columnar Storage: Storing data by columns rather than rows, allowing for more efficient retrieval of specific fields/columns needed for analysis. Optimal Compression: Applying compression techniques tailored to each column's data type. Massive Parallel Processing: Distributing data processing tasks across multiple servers and cloud regions, leveraging horizontal scaling capabilities.

Columnar databases shine in scenarios where query efficiency and cost optimization are paramount. They reduce computing and storage costs by processing less data and using efficient compression methods while boosting performance. For example, it is easy to find examples of columnar databases showing impressive performance results on data compressed from 5 GB to 300MB.

The combination of compute and storage reduction leads to better performance and lower cost per query. In addition, embracing the cloud provides improved accessibility, online analytics available anywhere, anytime.

In observability, where real-time analytics is crucial, columnar databases are valuable. They handle fast-growing data with fewer columns, making them ideal for storing logs, events, and traces. Observability platforms benefit from the performance and scalability offered by columnar databases, ensuring efficient and cost-effective data analysis.

Observability is evolving beyond simple dashboards. It's about gaining deep, real-time insights into complex systems. With their efficient storage and lightning-fast queries, Columnar databases are candidates for these needs.

While observability platforms may utilize various database types like time-series, graph, and vector databases, columnar databases can be used for logs, events, and traces, and they may take on more in the future. This is an exciting time for observability, focusing on cost optimization and performance maximization. Columnar databases are undoubtedly part of this conversation.

I have a successful app in the Android Market. One main functionality is that it records sports stats from a game the user is scoring. The current level of detail is fairly basic: a row for each player and a field for each basic stat. However, I could conceivably dramatically increase the detail and level of usefulness of the app if I recorded additional information, blowing this up to numerous relational tables and conceivably thousands upon thousands of records.

My question is, is this a responsible thing to do? Up to this point, I've shied away from it, thinking that "it's just a phone" and "it's just SQLite", but I've never really looked at whether that's a legitimate reason to hold back on doing things that I wouldn't give a second thought to doing on a web or desktop app.

EDIT:To be clear, I'm not simply talking about adding more fields as I know the impact of that is trivial. I'm talking about going from the level of detail of "This player has 5 singles and 3 homers" to storing information about each pitch that comprised each at-bat that lead to 5 singles and three homers. Obviously this will call for additional tables and conceivably a great many records.

For my masters thesis project at CMU, I worte an Android app that collected roughly 300mb (2.5 Million rows in the main table) of data during one day in sqlite. It drained the battery of the phone in about 10 hours and CPU utilization was at around 50%, but nothing of that had a lot to do with data. We were doing online learning on physiological data coming in over bluetooth with 72Hz. With the math intensive parts cut out, the phone was quite fine and useabel while the service was running. And the 10 hours were mostly due to bluetooth running continously. Without bluetooth, we got about 16-18 hours of continous uptime. Which I don't even get these days on the HTC Desire.

I think you're fine if you stay in the Megabytes or maybe low 10's of megabytes. As long as you have a a good design and don't do expensive querys in the main thread. SQLite handles well formed queries quite well, and dumping data into it is REALLY fast.

[edit:] just thought of something: Why don't you provide a second version of your app with the additional fields? More calculation obviously means a bit more battery drain, so I would give users some kind of choice. But my intution is that the performance hog won't really be noticeable.

Sounds like SQLite will do just fine for what you need. I wouldn't hesitate to push it's limits. It's a very well implemented product and should be able to handle what you describe. If you get to the point where your application is handling larger volumes of data (more than 100-200MB), you might want to consider Berkeley DB. Berkeley DB supports the SQLite3 API and provides additional data management capabilities that provide better performance, scalability and reliability than native SQLite, especially when dealing with larger data sets. - Dave

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