diff --git a/_data/events.yaml b/_data/events.yaml
index d4c0e1c..090e7c8 100644
--- a/_data/events.yaml
+++ b/_data/events.yaml
@@ -1,4 +1,11 @@
 
+- time: 2024-11-11 17:00:00
+  title: "Ubuntu for AI/ML projects"
+  speakers: [andreeamunteanu]
+  type: podcast
+  link: https://lu.ma/c5qa10su
+
+
 - time: 2024-09-26 17:00:00
   title: "DataTalks.Club Anniversary Podcast"
   speakers: [alexeygrigorev]
diff --git a/_podcast/s19e01-using-data-to-create-liveable-cities.md b/_podcast/s19e01-using-data-to-create-liveable-cities.md
new file mode 100644
index 0000000..b3832b0
--- /dev/null
+++ b/_podcast/s19e01-using-data-to-create-liveable-cities.md
@@ -0,0 +1,783 @@
+---
+episode: 1
+guests:
+- rachellim
+ids:
+  anchor: atatalksclub/episodes/Using-Data-to-Create-Liveable-Cities---Rachel-Lim-e2qecup
+  youtube: VXQIGHUWeL0
+image: images/podcast/s19e01-using-data-to-create-liveable-cities.jpg
+links:
+  anchor: https://podcasters.spotify.com/pod/show/datatalksclub/episodes/Using-Data-to-Create-Liveable-Cities---Rachel-Lim-e2qecup
+  apple: https://podcasts.apple.com/us/podcast/using-data-to-create-liveable-cities-rachel-lim/id1541710331?i=1000675373908
+  spotify: https://open.spotify.com/episode/1z7jdogto8i4Zk6Zh1vDxE?si=KCg2Iq1US0SKwFCKasGqUg
+  youtube: https://www.youtube.com/watch?v=VXQIGHUWeL0
+season: 19
+short: Using Data to Create Liveable Cities
+title: Using Data to Create Liveable Cities
+transcript:
+- header: Using data to create livable cities
+- line: This week, we'll talk about using data to create livable cities. We have a
+    special guest today, Rachel Lim. Rachel is an urban data scientist dedicated to
+    creating livable cities through the innovative use of data. Welcome, Rachel!
+  sec: 116
+  time: '1:56'
+  who: Alexey
+- line: Thank you! I'm happy to be here today. I've benefited greatly from the DataTalks.Club
+    courses, and I'm excited to share my experiences.
+  sec: 151
+  time: '2:31'
+  who: Rachel
+- line: We're happy to have you. Before diving into our main topic, could you tell
+    us about your career journey so far?
+  sec: 161
+  time: '2:41'
+  who: Alexey
+- header: 'Rachel''s career journey: from geography to urban data science'
+- line: Yes, I'm currently working as a data engineer in Singapore, focusing on creating
+    livable cities using data. My background is in geography — I have a bachelor's
+    degree in geography and a master's in urban data science. I blend qualitative
+    and quantitative analysis to tackle urban challenges.
+  sec: 172
+  time: '2:52'
+  who: Rachel
+- line: I began my career in data science, applying analytics and machine learning
+    to various transportation projects, such as bike-sharing analytics to address
+    indiscriminate parking and road defect detection using computer vision. These
+    projects allowed me to make a tangible impact in cities. Seeing my work lead to
+    real-world solutions motivated me to become a transport scientist. I focus on
+    analyzing travel patterns to support long-term planning in Singapore. Recently,
+    I transitioned to data engineering after completing the DataTalks.Club data engineering
+    course. By diving into data foundations and building data platforms, I aim to
+    optimize AI applications for creating livable cities.
+  sec: 172
+  time: '2:52'
+  who: Rachel
+- line: That sounds amazing! How was the course?
+  sec: 242
+  time: '4:02'
+  who: Alexey
+- line: It was really good. It covered a lot about building data pipelines and using
+    tools like Apache Kafka, which was quite an eye-opener. The course was very relevant
+    and helpful in my transition to my current role.
+  sec: 245
+  time: '4:05'
+  who: Rachel
+- header: What does a transport scientist do?
+- line: You mentioned you were a transport scientist. That’s an interesting title.
+    What exactly does a transport scientist do, and what types of organizations need
+    this role?
+  sec: 260
+  time: '4:20'
+  who: Alexey
+- line: Transport scientists are usually needed in the public sector, especially in
+    government agencies involved in transportation planning. The role involves applying
+    data science in a practical way to public transport and transportation planning.
+    Another sector where transport scientists are valuable is transport consultancy,
+    such as firms like Sam Schwartz. It's essentially about applying data science
+    within an urban context to improve transportation systems.
+  sec: 287
+  time: '4:47'
+  who: Rachel
+- line: So, is it about planning where to put bus stops, how often buses should run,
+    and similar things?
+  sec: 323
+  time: '5:23'
+  who: Alexey
+- header: Short-term and long-term transportation planning
+- line: Yes, that’s part of it. We separate our work into short-term and long-term
+    planning. In the short term, we look at bus routes, service frequencies, travel
+    patterns, and how well services are meeting users' needs. In the long term, we
+    use travel pattern data to make projections and plan for future infrastructure
+    needs, such as additional roads or rail lines and their alignment.
+  sec: 334
+  time: '5:34'
+  who: Rachel
+- header: Data sources for transportation planning in Singapore
+- line: I guess each bus in Singapore has sensors to track its location and passenger
+    load, right? This data helps you see if certain routes are overcrowded and need
+    more frequent service?
+  sec: 374
+  time: '6:14'
+  who: Alexey
+- line: Exactly. We use a combination of data sources. Buses are equipped with GPS
+    transponders, allowing us to track their locations and times at each bus stop.
+    This helps us identify issues like bus bunching, where multiple buses arrive at
+    the same stop simultaneously. Ideally, buses should be spaced out to optimize
+    service.
+  sec: 407
+  time: '6:47'
+  who: Rachel
+- line: On the demand side, we look at fare card data to understand where passengers
+    are tapping in and out, giving us a clearer picture of travel demand.
+  sec: 407
+  time: '6:47'
+  who: Rachel
+- line: What do you mean by "tapping in and out"?
+  sec: 456
+  time: '7:36'
+  who: Alexey
+- line: In Singapore, we use a fare card system similar to London's Oyster card or
+    New York's MetroCard. Passengers tap their card when they enter and exit public
+    transportation, like trains and buses.
+  sec: 460
+  time: '7:40'
+  who: Rachel
+- line: So there's a card and a reader? That makes sense. In Berlin, people can just
+    hop on a bus without any interaction. Sometimes there's a fare check, but it's
+    not consistent.
+  sec: 479
+  time: '7:59'
+  who: Alexey
+- line: Yes, that approach makes it more challenging to collect travel data. You’d
+    need to rely on video surveillance and computer vision to analyze passenger flow,
+    which is more complex than just processing fare card events.
+  sec: 500
+  time: '8:20'
+  who: Rachel
+- header: Rachel's motivation for combining geography and data science
+- line: Definitely. So, what motivated you to work at the intersection of geography
+    and data science?
+  sec: 518
+  time: '8:38'
+  who: Alexey
+- line: Growing up in Singapore, I was fascinated by the systems shaping our cities.
+    I witnessed firsthand how the city rapidly expanded its MRT (Mass Rapid Transit)
+    network and developed new housing estates. This sparked my interest in urban planning
+    and geography.
+  sec: 535
+  time: '8:55'
+  who: Rachel
+- line: I did an internship at the Center for Livable Cities, which deepened my understanding
+    of sustainable urban environments. During this time, I attended the World City
+    Summit, where city leaders from around the world shared ideas on improving urban
+    spaces.
+  sec: 535
+  time: '8:55'
+  who: Rachel
+- line: This inspired me to study geography, focusing on urban design, geocomputation,
+    and geospatial analytics. Eventually, this led me to pursue a master's in urban
+    informatics at New York University, specializing in applying data science in urban
+    contexts. This combination of experiences has shaped my current role.
+  sec: 535
+  time: '8:55'
+  who: Rachel
+- header: Urban design and its connection to geography
+- line: That's interesting. My knowledge of geography is mostly from school, where
+    we learned things like capital cities and natural features. I didn't realize geography
+    could involve urban design. Is urban design about planning new districts, including
+    schools and parks?
+  sec: 619
+  time: '10:19'
+  who: Alexey
+- line: Yes, but it goes beyond just planning where things go. It’s about designing
+    environments that are livable. This involves making streets walkable, deciding
+    on the width of streets, placing sidewalks, and using elements like planter boxes
+    to separate pedestrians from traffic. It's also about creating safe, welcoming
+    spaces where people want to linger and interact, fostering a sense of community.
+  sec: 686
+  time: '11:26'
+  who: Rachel
+- line: I see. Why is this field still called geography when it covers urban design
+    and other aspects?
+  sec: 726
+  time: '12:06'
+  who: Alexey
+- line: Geography isn't just about physical features like mountains or rivers. It
+    also includes human geography, which focuses on people, migration, and population
+    changes. It’s about how these factors interact with physical spaces. Geography
+    is fascinating because it’s connected to the real world — what we study directly
+    influences how we live and interact with our environments.
+  sec: 740
+  time: '12:20'
+  who: Rachel
+- header: Defining a livable city
+- line: So far, we’ve talked about what makes a city livable — parks, pedestrian zones,
+    traffic management, and fostering community. How would you define a livable city?
+  sec: 792
+  time: '13:12'
+  who: Alexey
+- line: A livable city is one where people feel connected to their community and have
+    opportunities to thrive. In terms of the built environment, this can include efficient
+    public transport with a well-connected network of buses, trains, and bike-sharing
+    programs. It also means safe, pedestrian-friendly streets with dedicated bike
+    lanes and walkways.
+  sec: 829
+  time: '13:49'
+  who: Rachel
+- line: Other aspects are affordable housing, proximity to essential services, and
+    green spaces. Beyond physical infrastructure, digital infrastructure plays a role
+    too. This includes online access to government services, digital safety, and platforms
+    that connect residents, recognizing that people spend a lot of time in digital
+    spaces now.
+  sec: 829
+  time: '13:49'
+  who: Rachel
+- header: Livability of Singapore and urban planning
+- line: How livable is Singapore, in your opinion? I've never been there, but it's
+    on my list.
+  sec: 930
+  time: '15:30'
+  who: Alexey
+- line: Singapore has made significant progress. Initially, we focused on developing
+    housing estates, but now there's a greater emphasis on placemaking — creating
+    spaces where people can gather and enjoy. We're also converting certain streets
+    into car-free zones, improving walkability and cycling infrastructure. It's a
+    journey; some parts of the city are more livable than others, but we're working
+    to expand these spaces.
+  sec: 948
+  time: '15:48'
+  who: Rachel
+- line: Singapore is geographically small and densely populated, so I imagine land
+    use has to be very efficient.
+  sec: 995
+  time: '16:35'
+  who: Alexey
+- line: Absolutely. Singapore practices a process called Master Planning, which involves
+    planning 15 years ahead. This ensures that amenities and infrastructure are effectively
+    integrated over time.
+  sec: 1015
+  time: '16:55'
+  who: Rachel
+- line: Interesting. I live in Berlin, and I think the city is fairly livable. It
+    has good public transport, though cycling infrastructure could be improved. In
+    Moscow, it's harder for people with disabilities to get around, whereas Berlin
+    is more accommodating. It’s fascinating to see the practical application of geography
+    and data science. Could you share more about what you do as a transport scientist
+    and now as a data engineer?
+  sec: 1036
+  time: '17:16'
+  who: Alexey
+- header: Role of data science in urban and transportation planning
+- line: Planning a city requires deliberate effort, and data science plays a critical
+    role in improving livability by offering insights and supporting data-driven decision-making.
+    By analyzing data collected throughout the city, we can optimize services, enhance
+    public safety, and promote sustainability.
+  sec: 1104
+  time: '18:24'
+  who: Rachel
+- line: In Singapore, we collect a lot of public data, which we share on open data
+    platforms. This enables collaboration with citizen developers, students, and research
+    institutions. Our data sources include transportation data like fare card usage,
+    as well as census and survey data. This helps us understand travel patterns and
+    conduct transport modeling to plan future infrastructure, such as rail lines.
+  sec: 1104
+  time: '18:24'
+  who: Rachel
+- line: We're also increasingly using data from the private sector, like mobility
+    data from ride-sharing apps. This gives us additional insights into how people
+    move around the city, beyond just public transportation.
+  sec: 1104
+  time: '18:24'
+  who: Rachel
+- header: Predicting travel patterns for future transportation needs
+- line: That's interesting because you mentioned predicting where people will move.
+    If I understood correctly, for instance, if more people start moving from one
+    part of Singapore to another, you can anticipate this trend. Maybe a district
+    is becoming more popular, so it's gradually getting more populated. You want to
+    predict these patterns to plan accordingly, right? Like adding a new bus line
+    or increasing the frequency of existing buses?
+  sec: 1231
+  time: '20:31'
+  who: Alexey
+- line: Yes, that's right. Singapore is quite small, so we actively plan how housing
+    estates will develop. This could involve building new housing estates or renewing
+    and rejuvenating existing areas. By doing so, we can estimate how many people
+    will live in a particular district. From there, we use past travel patterns and
+    data on new modes of transport to predict future movements. This helps us plan
+    ahead for necessary transportation services, such as adding new bus lines or stops
+    to ensure these areas are well-connected to the rest of Singapore.
+  sec: 1269
+  time: '21:09'
+  who: Rachel
+- header: Data collection and processing in transportation systems
+- line: I see. As a data scientist, you can't do much without data. You mentioned
+    various data types, like sensor data, movement patterns, and ride-hailing information.
+    All of this needs to be collected, processed, and analyzed. This must involve
+    having sensors on buses and other physical means of data collection, right? Then,
+    this data needs to be sent to a platform, aggregated, processed, and perhaps stored
+    in a data warehouse for data scientists to use. As a data engineer, you're probably
+    involved in these steps. Can you tell us more about what happens behind the scenes?
+  sec: 1322
+  time: '22:02'
+  who: Alexey
+- line: Yes, we work with a combination of data sources. We gather GPS data from ride-hailing
+    companies and public transport, along with fare card information about when and
+    where people are tapping in and out. In our data pipelines, we have an end-to-end
+    system that aggregates this information, stores it in a data warehouse, and processes
+    it so that it's suitable for downstream data analysis. We don't just need real-time
+    data; we also require historical data to do projections. Long-term data allows
+    us to track patterns over time, which is crucial for providing stable and reliable
+    insights.
+  sec: 1381
+  time: '23:01'
+  who: Rachel
+- header: Use of real-time data for traffic management
+- line: Are there situations where you actually need real-time data as well?
+  sec: 1442
+  time: '24:02'
+  who: Alexey
+- line: Yes, real-time data is often needed for managing operations. For example,
+    to monitor the reliability of services, real-time data is essential. Another use
+    case is tracking traffic flow during specific events. In Singapore, we host the
+    F1 night race, which takes place on a street circuit, so parts of the roads are
+    closed. We use taxi data as a proxy to understand how traffic is flowing around
+    the closed areas. By monitoring how quickly the GPS coordinates from taxis are
+    moving, we can detect congestion and adjust traffic management strategies accordingly.
+  sec: 1449
+  time: '24:09'
+  who: Rachel
+- line: What actions are taken if there's a traffic jam?
+  sec: 1507
+  time: '25:07'
+  who: Alexey
+- line: It depends on the location. If a traffic jam occurs on an expressway, we have
+    cameras that monitor these areas, pinpointing where the issue is. Recovery services,
+    like tow trucks or other assistance, are dispatched to manage the situation and
+    clear the blockage.
+  sec: 1510
+  time: '25:10'
+  who: Rachel
+- line: So it could involve police officers managing traffic or using other traffic
+    marshals to control the situation?
+  sec: 1529
+  time: '25:29'
+  who: Alexey
+- line: Yes, a combination of traffic marshals and recovery services are deployed
+    to clear any blockages and ensure that traffic can flow smoothly again.
+  sec: 1535
+  time: '25:35'
+  who: Rachel
+- line: In Berlin, we often have events like half marathons or marathons that require
+    large streets to be closed for half a day. This can be quite inconvenient for
+    drivers who need to find alternative routes. These events cover large distances,
+    so multiple roads must be blocked. Do you have similar events in Singapore, and
+    how do you handle them?
+  sec: 1545
+  time: '25:45'
+  who: Alexey
+- line: Yes, we host marathons and similar events in Singapore. These events often
+    use a mix of roads and our network of park connectors, which are pathways that
+    connect different parks and are free of vehicles. This allows most of the marathon
+    routes to take place without significantly disrupting traffic on main roads. The
+    impact on traffic is minimized because only a few roads need to be closed, thanks
+    to these park connectors.
+  sec: 1590
+  time: '26:30'
+  who: Rachel
+- header: Incorporating generative AI into data engineering
+- line: That makes sense. Having connected parks where people can run without needing
+    to wait for traffic lights is an excellent way to make a city more livable. I'm
+    into running myself, and finding a route without having to wait for traffic lights
+    is always a challenge. It's great that Singapore has planned these aspects so
+    well. Do you participate in organizing these events, or are you more focused on
+    transportation in your role?
+  sec: 1626
+  time: '27:06'
+  who: Alexey
+- line: My role is more focused on transportation. While I don't directly manage these
+    events, I work on data preparation and building data pipelines, ensuring that
+    transportation systems run smoothly. Increasingly, I'm also looking at ways to
+    incorporate generative AI into data engineering, such as enabling users to query
+    databases without needing SQL knowledge.
+  sec: 1679
+  time: '27:59'
+  who: Rachel
+- line: How often do people need to make these types of queries?
+  sec: 1723
+  time: '28:43'
+  who: Alexey
+- line: Quite often. As data engineers or scientists, we frequently receive requests
+    to extract data or perform specific analyses. Building tools that allow users
+    to access insights on their own would free up our time for more innovative work.
+    I believe that subject matter experts should drive data science and analysis because
+    they best understand their needs and the problems they want to solve. When problems
+    and requests are passed through multiple people, the original intent can sometimes
+    get diluted. Clear, well-defined problem statements are essential for effective
+    analysis.
+  sec: 1728
+  time: '28:48'
+  who: Rachel
+- line: Who are the people making these requests, usually? I assume they are subject
+    matter experts who aren't necessarily technical, right?
+  sec: 1793
+  time: '29:53'
+  who: Alexey
+- header: Data analysis for transportation policies
+- line: Yes, they could be subject matter experts, like those working in policy-making.
+    They need data to develop data-driven policies.
+  sec: 1809
+  time: '30:09'
+  who: Rachel
+- line: Can you give an example of a policy that might need data analysis?
+  sec: 1821
+  time: '30:21'
+  who: Alexey
+- line: Sure, one example is analyzing fare card data to determine appropriate transportation
+    pricing. We might want to know how many people use concession cards, like those
+    for senior citizens or students, to evaluate the effectiveness of a new policy
+    or fare adjustment.
+  sec: 1827
+  time: '30:27'
+  who: Rachel
+- line: What is a concession card?
+  sec: 1871
+  time: '31:11'
+  who: Alexey
+- line: A concession card provides discounted fares. In Singapore, for instance, senior
+    citizens and students can use these cards to pay reduced rates on public transportation.
+  sec: 1860
+  time: '31:00'
+  who: Rachel
+- line: So, policy questions might include things like what happens if we increase
+    fares or introduce a new ticket option? For example, in Berlin, there are discounted
+    bundles of tickets. You might need data to predict the impact of similar changes.
+  sec: 1871
+  time: '31:11'
+  who: Alexey
+- line: Exactly. In Singapore, we have monthly passes offering unlimited travel for
+    a fixed price, with specific options for students. We might analyze how many students
+    use these passes and how effective they are in encouraging public transport use.
+  sec: 1897
+  time: '31:37'
+  who: Rachel
+- line: And since people need to tap their cards to use transportation, you have all
+    this data. But policy specialists might not know how to query it. Are you working
+    on making this easier for them, perhaps through a chat interface that translates
+    their questions into SQL queries?
+  sec: 1925
+  time: '32:05'
+  who: Alexey
+- line: Yes, that's something we're looking to build. It's still in development, but
+    we're working on tools that can help extract information and perform analysis
+    more intuitively, potentially using plain language queries.
+  sec: 1955
+  time: '32:35'
+  who: Rachel
+- line: Is this a project you're actively working on now?
+  sec: 1973
+  time: '32:53'
+  who: Alexey
+- line: Yes, it's one of the projects we're currently focusing on.
+  sec: 1979
+  time: '32:59'
+  who: Rachel
+- header: Technologies used in text-to-SQL projects
+- line: What technologies or approaches are you using for this project? It sounds
+    like a fascinating application of large language models (LLMs), and it might interest
+    our LLM Zoomcamp students to learn about its practical uses.
+  sec: 1999
+  time: '33:19'
+  who: Alexey
+- line: In the text-to-SQL space, we're looking at using metadata from data warehouses
+    and data catalogs. We chunk this information and create a vector database, then
+    apply a large language model (LLM) on top of it. The LLM takes user queries in
+    plain English, translates them into SQL statements, and returns outputs to the
+    users.
+  sec: 2028
+  time: '33:48'
+  who: Rachel
+- line: So, it's like a retrieval-augmented generation (RAG) setup. A user provides
+    a plain English query, and you use context from metadata to generate the SQL query.
+    The LLM then executes the query and returns the results. Is that how it works?
+  sec: 2040
+  time: '34:00'
+  who: Alexey
+- line: Yes, exactly. We use RAG techniques. The metadata helps the LLM understand
+    which tables to refer to for the correct information extraction.
+  sec: 2083
+  time: '34:43'
+  who: Rachel
+- line: How often do the queries generated by this process fail or need correction?
+  sec: 2109
+  time: '35:09'
+  who: Alexey
+- line: I don't have exact numbers, but it does happen. Success largely depends on
+    effective prompt engineering. Providing sufficient examples of text-to-SQL conversions
+    helps guide the LLM. It’s also important to restrict certain types of SQL commands,
+    like insert, update, or delete statements, to prevent unintended database modifications.
+  sec: 2118
+  time: '35:18'
+  who: Rachel
+- header: Handling large datasets and transportation data in Singapore
+- line: That's a fascinating project. I'm always keen to learn about new LLM use cases
+    because there are so many possibilities. It's great that you're exploring this
+    space too. This project is in an early development phase, right?
+  sec: 2172
+  time: '36:12'
+  who: Alexey
+- line: Yes, it’s still in early development.
+  sec: 2190
+  time: '36:30'
+  who: Rachel
+- line: How large are the datasets you're working with for these projects? Besides
+    the text-to-SQL project, are there other more established projects you're involved
+    in? You mentioned transportation data — like fare card data. How large are these
+    datasets?
+  sec: 2192
+  time: '36:32'
+  who: Alexey
+- line: We collect a significant amount of data. For fare card data alone, we gather
+    millions of passenger flow records daily in Singapore. This provides numerous
+    data points for analyzing passenger movements, identifying peak travel times,
+    and assessing route popularity. This data is crucial for optimizing routes and
+    fare structures.
+  sec: 2231
+  time: '37:11'
+  who: Rachel
+- line: For example, in Singapore, the Public Transport Council implemented a "morning
+    pre-peak fare" policy, offering savings for commuters who travel before 7:45 AM.
+    This aims to shift demand away from peak hours, balancing train load and encouraging
+    earlier travel. By analyzing fare card data, we can evaluate the effectiveness
+    of such policies.
+  sec: 2231
+  time: '37:11'
+  who: Rachel
+- line: Regarding these millions of data points you collect, how are they processed?
+    Do you use standard tools like Kafka, data lakes, and data warehouses? Also, how
+    does data from the buses get collected? Are there transmitters on the buses that
+    send data to Kafka or another system?
+  sec: 2314
+  time: '38:34'
+  who: Alexey
+- line: Yes, we have sensors on the buses that collect location information. We also
+    gather data from fare card systems at the entry and exit points.
+  sec: 2344
+  time: '39:04'
+  who: Rachel
+- line: Is this data collected in real-time? For example, the moment I tap my card,
+    does an event get recorded immediately, or is the data only gathered at the end
+    of the day?
+  sec: 2354
+  time: '39:14'
+  who: Alexey
+- line: The data is collected in real-time, but aggregation happens afterward. In
+    Singapore, we use a system that defines a ride and a journey. Our fare structure
+    allows commuters to make multiple transfers within a 45-minute period, which is
+    considered one journey. We calculate fares based on the total distance traveled.
+    So, while data is collected in real-time, processing and aggregation occur after
+    a time lag. This allows us to combine all this information before storing it in
+    a data warehouse. We use tools like Kafka and Apache Spark for processing.
+  sec: 2367
+  time: '39:27'
+  who: Rachel
+- line: So, the events from the bus are sent immediately to your system. You don't
+    need to wait until the bus completes its shift to connect and download the data,
+    right?
+  sec: 2417
+  time: '40:17'
+  who: Alexey
+- line: Yes, that's correct.
+  sec: 2436
+  time: '40:36'
+  who: Rachel
+- line: That's great. You use Kafka, Apache Spark, and other common tools in data
+    engineering, right?
+  sec: 2440
+  time: '40:40'
+  who: Alexey
+- line: Yes, that's right.
+  sec: 2448
+  time: '40:48'
+  who: Rachel
+- line: Have you ever had to go on a bus to fix a sensor?
+  sec: 2460
+  time: '41:00'
+  who: Alexey
+- line: No, I haven't needed to do that.
+  sec: 2464
+  time: '41:04'
+  who: Rachel
+- line: So, the sensors are quite reliable, right?
+  sec: 2468
+  time: '41:08'
+  who: Alexey
+- line: Yes, generally they are reliable. However, part of data engineering involves
+    detecting data quality issues or anomalies. For instance, if a transponder on
+    a bus isn’t sending data correctly, we need to identify that issue. While I haven't
+    personally gone to a bus to fix a sensor, maintaining data quality is a critical
+    aspect of data engineering.
+  sec: 2468
+  time: '41:08'
+  who: Rachel
+- line: I'm considering whether to dive deeper into traditional data engineering topics
+    or explore generative AI applications. What would you like to focus on next?
+  sec: 2506
+  time: '41:46'
+  who: Alexey
+- line: We could go more into how AI is being used.
+  sec: 2531
+  time: '42:11'
+  who: Rachel
+- header: Generative AI applications beyond text-to-SQL
+- line: Do you have other AI applications besides the text-to-SQL tool?
+  sec: 2537
+  time: '42:17'
+  who: Alexey
+- line: The text-to-SQL tool is my main focus right now. However, generative AI has
+    other potential applications, such as creating synthetic data. In projects where
+    we don't have a full data set, generative AI could help generate synthetic data.
+    Additionally, I believe generative AI could redefine user interfaces, making information
+    retrieval more conversational and intuitive, moving away from traditional keyword
+    searches to semantic searches. This approach could change how people search for
+    things, not just in e-commerce but in various domains, like planning what to buy
+    for a gift.
+  sec: 2548
+  time: '42:28'
+  who: Rachel
+- line: I'm becoming more accustomed to using tools like ChatGPT with voice recognition.
+    It's very convenient. Regarding synthetic data, how effective is generative AI
+    at creating data, especially numerical data or time series data?
+  sec: 2635
+  time: '43:55'
+  who: Alexey
+- line: I was speaking generally about the potential of generative AI for creating
+    synthetic data, especially when dealing with complex or sensitive data sets. Generative
+    AI could help create synthetic versions that mask confidential information while
+    retaining essential characteristics.
+  sec: 2682
+  time: '44:42'
+  who: Rachel
+- header: Publishing public data and maintaining privacy
+- line: Since Singapore releases a lot of public data on open platforms, sometimes
+    you might need to edit this data before publishing it, right?
+  sec: 2726
+  time: '45:26'
+  who: Alexey
+- line: The current publicly shared data is collected from various systems, and we
+    mask sensitive information, such as fare card numbers, before publishing.
+  sec: 2740
+  time: '45:40'
+  who: Rachel
+- header: Recommended datasets and projects for data engineering beginners
+- line: Where can people find this public data?
+  sec: 2752
+  time: '45:52'
+  who: Alexey
+- line: 'Two main platforms provide public data in Singapore: data.gov.sg and DataMall.
+    Data.gov.sg aggregates data from various government bodies, covering areas like
+    rainfall, air pollution, transportation, and more.'
+  sec: 2760
+  time: '46:00'
+  who: Rachel
+- line: There are categories like arts, education, economy, environment, housing,
+    health, social, transport, and real-time APIs. I assume I should look under the
+    transport category to find relevant data, right?
+  sec: 2780
+  time: '46:20'
+  who: Alexey
+- line: Yes, you’ll find transportation data, such as air travel and geospatial information,
+    under the transport category.
+  sec: 2801
+  time: '46:41'
+  who: Rachel
+- line: If someone is starting the next cohort of a data engineering course in January,
+    what kinds of projects would you recommend they try using these data sets?
+  sec: 2810
+  time: '46:50'
+  who: Alexey
+- line: One useful data set could be car parking data, as we collect real-time parking
+    transaction data. It's a large and dynamic data set, ideal for real-time data
+    ingestion, storage in a data warehouse or data lake, and subsequent analysis.
+  sec: 2825
+  time: '47:05'
+  who: Rachel
+- line: How do I find the car parking data?
+  sec: 2856
+  time: '47:36'
+  who: Alexey
+- line: Car parking data is typically part of our dynamic data sets.
+  sec: 2867
+  time: '47:47'
+  who: Rachel
+- line: There are real-time APIs. Maybe that’s what I need to look for. Could you
+    send me the link later?
+  sec: 2871
+  time: '47:51'
+  who: Alexey
+- line: Sure, I can send it to you. Another valuable data set is real-time taxi data,
+    which can be useful for examining data engineering processes. In previous data
+    engineering courses, we used the New York taxi data set, which is aggregated.
+    Real-time data offers another layer of complexity and learning opportunities.
+  sec: 2888
+  time: '48:08'
+  who: Rachel
+- line: I see public transport capacity, taxi population data, and more, some of which
+    are updated regularly. You've sent me another link, right?
+  sec: 2915
+  time: '48:35'
+  who: Alexey
+- line: Yes, I've sent another link, specifically for transportation data under the
+    dynamic data sets, including real-time taxi availability and parking information.
+  sec: 2943
+  time: '49:03'
+  who: Rachel
+- header: Recommended resources for learning urban data science
+- line: Great, I'll add this link to our description. Thanks. I don't see any questions
+    from the audience at the moment. I'm wondering if someone wants to study what
+    you do — urban data science and transport planning — what resources, books, or
+    courses would you recommend?
+  sec: 2956
+  time: '49:16'
+  who: Alexey
+- line: 'The courses by DataTalks.Club are a good primer. For a deeper understanding
+    of urban data science, the book "The Death and Life of Great American Cities"
+    by Jane Jacobs is a classic. It critiques traditional urban planning and advocates
+    for vibrant, livable cities through community-based approaches and human-scale
+    design. Another book is "Happy City: Transforming Our Lives Through Urban Design"
+    by Charles Montgomery. This book blends urban planning with psychology and sociology,
+    exploring how thoughtful urban design can improve happiness and well-being. Both
+    books offer insights into why we study urban data and plan cities thoughtfully.'
+  sec: 3022
+  time: '50:22'
+  who: Rachel
+- line: Those sound interesting. The first one is "The Death and Life of Great American
+    Cities," and the second is "Happy City," right?
+  sec: 3102
+  time: '51:42'
+  who: Alexey
+- line: Yes, that's correct.
+  sec: 3120
+  time: '52:00'
+  who: Rachel
+- line: I remember visiting the United States, where getting around without a car
+    is challenging, except in cities like New York or Boston. It would be great if
+    some of the practices from Europe or Singapore were implemented there. I recall
+    once being picked up by the police while walking down a road without sidewalks
+    because I wasn't used to the idea that you need a car to move around.
+  sec: 3125
+  time: '52:05'
+  who: Alexey
+- line: That's quite amusing!
+  sec: 3160
+  time: '52:40'
+  who: Rachel
+- line: Anyway, Rachel, thank you so much for joining us today and sharing your experiences.
+    I’ve learned a lot. These topics were new to me, and it was enlightening to hear
+    about your work. Thanks for being here, and thanks to everyone else who joined
+    us today. This was our first episode after a break, and it was a great start.
+  sec: 3162
+  time: '52:42'
+  who: Alexey
+- line: Thank you for having me.
+  sec: 3189
+  time: '53:09'
+  who: Rachel
+- line: I'm happy to hear about your transition to data engineering and that our course
+    helped. It means a lot to see success stories like yours. Thanks again!
+  sec: 3191
+  time: '53:11'
+  who: Alexey
+- line: Thank you.
+  sec: 3206
+  time: '53:26'
+  who: Rachel
+- line: Have a wonderful rest of your day, Rachel, and to everyone else — see you
+    around!
+  sec: 3208
+  time: '53:28'
+  who: Alexey
+---
+
+Links:
+
+* [Dynamic Datasets](https://datamall.lta.gov.sg/content/datamall/en/dynamic-data.html){:target="_blank"}
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