As Manager: Data Analytics at xyz you will deliver data-driven solutions, identify business opportunities, and use analytics and predictive models to enhance decision-making. You will turn strategic plans into actionable insights and promote best practices.
By encouraging collaboration and using advanced solutions, you will help strengthen xyz's leadership in analytics and data-driven insights.
Over ten years of experience in a commercial setting, including four years in a team leadership or managerial capacity. Ideally within the telecommunications industry with a proven track record of delivering value through data-driven decision-making.
Proficient in SQL and delivering complex analytical requirements.
Managing senior and executive level stakeholders and influencing decision making at these levels
Experience with cloud platforms (AWS, Microsoft, Salesforce Data Cloud).
Experience with low/no code AutoML tools like Power BI or AWS Sagemaker.
Data visualisation with tools such as PowerBI
Big data frameworks – Hadoop/Kafka
Github experience or Git
Analytical and problem-solving skills.
Excellent communication and interpersonal skills.
Strong facilitation and presentation abilities.
Commercial and strategic thinking skills.
Attention to detail and accuracy.
Ability to drive innovation and simplify processes.
Completed a Bachelor's Degree in Commerce, Engineering, Mathematics, or Statistics.
Big Data Frameworks
Here are 100 must-know facts, concepts, and skills about Big Data frameworks — Hadoop and Kafka — divided into key sections for clarity:
Understand what Big Data means: the 5 Vs — Volume, Velocity, Variety, Veracity, Value.
Hadoop and Kafka both handle large-scale distributed data processing.
Hadoop is mainly for batch processing; Kafka is for real-time streaming.
Hadoop was inspired by Google File System (GFS) and MapReduce.
Kafka originated at LinkedIn for managing activity stream data.
Hadoop ecosystem includes HDFS, MapReduce, YARN, and many sub-projects.
Kafka ecosystem includes Producers, Consumers, Topics, Partitions, Brokers, and Zookeeper/KRaft.
Both systems are open source under the Apache Software Foundation.
Hadoop and Kafka integrate frequently — Kafka feeds real-time data into Hadoop for storage or analytics.
Both frameworks are horizontally scalable.
Hadoop provides fault tolerance via replication in HDFS.
Kafka provides fault tolerance via partition replication across brokers.
Hadoop is disk-based; Kafka is log-based.
Hadoop uses batch jobs; Kafka supports stream processing.
Both rely on distributed cluster architectures.
HDFS stores very large files across multiple machines.
Data is split into blocks (default 128MB) and distributed.
Each block is replicated (default replication factor = 3).
NameNode manages metadata; DataNodes store actual data.
Secondary NameNode performs checkpointing, not redundancy.
HDFS uses write-once, read-many model.
Files are immutable once written.
Data locality is key — compute is moved to where the data resides.
HDFS can integrate with cloud storage via connectors.
Supports rack awareness for fault isolation.
MapReduce has two main phases: Map and Reduce.
Mapper transforms input data into key-value pairs.
Reducer aggregates or summarizes those pairs.
Intermediate data is shuffled and sorted between map and reduce.
Jobs are submitted via JobTracker (YARN now manages this).
Common use: log processing, word count, data aggregation.
Programmable in Java, Python, or Scala.
Inefficient for small, real-time tasks.
Replaced in many modern stacks by Spark.
Manages cluster resources and job scheduling.
Consists of ResourceManager and NodeManagers.
Allows multiple frameworks (Spark, Tez, MapReduce) to run on Hadoop.
Handles job priorities, memory, and CPU allocation.
Supports multi-tenancy.
Integrates with Kubernetes for containerized deployments.
Hive – SQL-like querying layer over Hadoop.
Pig – high-level scripting for data flow (now legacy).
HBase – NoSQL database on top of HDFS.
Sqoop – imports/export data between Hadoop and RDBMS.
Flume – ingests log data into HDFS.
Oozie – workflow scheduler for Hadoop jobs.
Zookeeper – coordination service for distributed apps.
Spark – in-memory data processing engine (Hadoop compatible).
Ambari – cluster provisioning and monitoring tool.
Mahout – machine learning library for Hadoop.
Kafka is a distributed publish-subscribe messaging system.
Core unit: Topic — a category or feed name for messages.
Each topic is split into Partitions for parallelism.
Each message has an offset — a unique sequential ID per partition.
Kafka stores messages durably on disk.
Kafka Brokers = Servers that handle data storage and serve clients.
Producer sends data to topics.
Consumer reads data from topics.
Consumers belong to Consumer Groups for load balancing.
Kafka ensures at-least-once message delivery by default.
Can be configured for exactly-once semantics.
Kafka supports retention policies — time- or size-based.
Uses log compaction to keep the latest state per key.
Kafka relies on Zookeeper (deprecated in new versions, replaced by KRaft).
Kafka supports Schema Registry for data consistency.
Kafka supports replay of events — you can reprocess data from past offsets.
High throughput — millions of messages per second.
Uses zero-copy I/O for performance.
Kafka brokers are stateless — metadata is stored in Zookeeper/KRaft.
Replication factor ensures fault tolerance across brokers.
Leader-Follower model for partition replication.
ISR (In-Sync Replica) – replicas that are up-to-date with the leader.
Producer acknowledgments (acks) can be set to 0, 1, or all.
Consumer offset management can be automatic or manual.
Kafka Connect allows integration with external systems (databases, HDFS, etc.).
Kafka Streams is the native stream processing library.
Kafka integrates with Spark Streaming, Flink, and Storm.
Kafka cluster scaling is achieved by adding more brokers.
Kafka supports TLS/SSL for encryption in transit.
Supports SASL for authentication.
Kafka can run on-prem or in the cloud (Confluent Cloud, AWS MSK, etc.).
Kafka monitoring via Prometheus, Grafana, or Confluent Control Center.
Kafka can handle both hot (real-time) and cold (batch) data feeds.
Exactly-once semantics (EOS) ensures no duplicate processing.
Common use cases: log aggregation, event sourcing, microservice communication, and IoT streams.
Kafka can feed Hadoop with real-time streams for later analysis.
HDFS can be used as long-term storage for Kafka topics.
Kafka + Spark = real-time analytics.
Hadoop + Hive + Kafka = data lake with real-time ingestion.
Kafka Connectors can write directly into HDFS or Hive tables.
Hadoop handles historical data; Kafka handles live feeds.
Use NiFi or Flume to bridge Kafka and Hadoop pipelines.
Typical architecture: IoT → Kafka → Spark → HDFS → Hive → BI.
Hadoop suits ETL, Kafka suits ELT pipelines.
Together, they support lambda architectures (batch + stream).
Monitor cluster health using Ambari (Hadoop) and Prometheus (Kafka).
Use Kerberos for Hadoop authentication.
Secure Kafka using SSL, SASL, and ACLs.
Optimize Kafka with proper partitioning and key design.
Tune Hadoop by managing block size, memory, and compression (Snappy, LZO).