Why Bodybuilders Love Dianabol PPT Hormonal Disorders Endocrine And  Metabolic Diseases  Here are a few project_idea starters that should fit  comfortably into an undergraduate capstone:    1. Machine_learning / data_science projects     Idea Why it_s doable in 2_3_months    Predictive maintenance for a small fleet of vehicles _ use sensor logs (speed, rpm, temperature)  to forecast when a part will likely fail. You can obtain public datasets or simulate data; the core ML pipeline is just a few regressors or classification models.    Sentiment analysis on product reviews _ build a model that predicts customer satisfaction from  review text. Plenty of free APIs (Amazon, Yelp) provide labeled data; you_ll mainly  work on feature extraction and model tuning.    House_price regression with the Boston Housing dataset _  explore feature importance and try different algorithms. Classic ML exercise; good for showcasing evaluation metrics and interpretability.    These projects let you demonstrate end_to_end data_science workflow without needing to build an entire system.      ---     2. Building a "Data_Science System"   When the interview expects a system_level design, they_re looking at how  you would turn those analytics into something that can be used by non_technical stakeholders, scaled to many users, and maintained over time.      Below is a step_by_step guide for an end_to_end data_science system.    Feel free to replace the concrete example (e.g., sales_forecasting  web app) with your own domain.     2.1 Problem Definition & Scope    Step What to Clarify Why    Business objective "Reduce inventory costs by forecasting demand." Sets KPI and success criteria.     User personas Store managers, supply_chain analysts. Determines UI/UX requirements.    Data volume & velocity 1_M sales records per year, real_time updates  from POS. Influences architecture choices.     2.2 Data Acquisition       Sources: ERP database (sales), supplier API (lead times), external market  indices.   Mechanism:   - Batch ETL via scheduled Airflow DAGs for historical data.   - Streaming ingestion with Kafka Connect for real_time POS  updates.     Sample code _ Kafka Producer for sales events    from kafka import KafkaProducer import json, time  producer = KafkaProducer( bootstrap_servers='kafka:9092', value_serializer=lambda v: json.dumps(v).encode('utf-8') )  while True: event =  'order_id': 123456, 'timestamp': int(time.time()), 'product_id': 987, 'quantity': 3, 'price': 19.99  producer.send('sales-events', value=event) time.sleep(1)    Data processing pipeline   Data is ingested into a Spark Structured Streaming job that aggregates sales by product and time window (e.g., 30_minute intervals).  The job writes aggregated results to a Delta Lake table,  which serves as the source for dashboards.     Spark Structured Streaming code snippet    from pyspark.sql import SparkSession from pyspark.sql.functions import window, sum as sum  spark = SparkSession.builder.appName("SalesAggregator").getOrCreate()  salesdf = spark.readStream.format("kafka") \ .option("kafka.bootstrap.servers", "broker1:9092") \ .option("subscribe", "sales_topic") \ .load() \ .selectExpr("CAST(value AS STRING) as json_str") \ .select(from_json(col("json_str"), sales_schema).alias("data")) \ .select("data.")  aggregated = sales_df.groupBy( window(col("timestamp"), "5 minutes"), col("store_id") ).agg(sum("amount").alias("total_sales"))  query = aggregated.writeStream \ .outputMode("update") \ .format("delta") \ .option("checkpointLocation", "/checkpoints/aggregated_sales") \ .start("/delta/aggregated_sales")   Wait until the streaming job completes  streaming_job.awaitTermination()   This code creates a Delta Lake table and writes aggregated sales data to it. You can customize this example according to your use case, such as adding more  columns, adjusting the aggregation logic, or writing the results  to different destinations.    Note that running Delta Lake requires a distributed environment, such as Apache  Spark, which can be set up on a cloud platform or on-premise  cluster.    Here is an example of how you could write code to handle the ingestion and processing  of streaming data from a message queue using Delta Lake:      from pyspark.sql import SparkSession from pyspark.sql.functions import col, expr   create Spark session  spark = SparkSession.builder \ .appName("DeltaLakeStreamingExample") \ .config("spark.jars.packages", "io.delta:delta-core_2.12:1.0.0") \ .getOrCreate()   set up the streaming source (e.g., Kafka, RabbitMQ, etc.)  streaming_source = spark.readStream.format("kafka") \ .option("kafka.bootstrap.servers", "localhost:9092") \ .option("subscribe", "my_topic") \ .load()   parse and transform the incoming data  parsed_data = streaming_source.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")  \ .selectExpr("key", "json_tuple(value, 'field1', 'field2') as (field1, field2)")   write the transformed data to a sink (e.g., database, file system, etc.)  query = parsed_data.writeStream.format("parquet") \ .option("path", "/tmp/output") \ .outputMode("append") \ .start()  query.awaitTermination()   In this example, we first read the data from a Kafka topic using  Spark's `readStream` API. We then use Spark SQL to transform the incoming JSON data into columns and write it out as Parquet files.     The key steps in this example are:      Reading the data from Kafka   Using Spark SQL to parse the incoming data and create a DataFrame   Writing the output using Spark's `writeStream` API    If you have a more specific use case or scenario, please let me know  and I can provide additional guidance.  I apologize for any confusion caused. It seems that my previous response was not relevant to  your question about writing data to a Kafka topic with  the Java Producer API.    Here is an example of how to write data to a Kafka topic  using the Java Producer API:     import java.util.Properties; import org.apache.kafka.clients.producer.;  public class KafkaProducerExample  public static void main(String args) throws Exception  // Set up producer properties Properties props = new Properties(); props.put("bootstrap.servers", "localhost:9092"); props.put("acks", "all"); props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");   // Create producer Producer producer = new KafkaProducer<>(props);  // Send a message to topic "test" String topic = "test"; String key = "key1"; String value = "Hello, world!"; ProducerRecord record = new ProducerRecord<>(topic, key, value); producer.send(record);  // Flush and close the producer producer.flush(); producer.close();     This program will send a single message to the  Kafka topic named "test". Make sure you have a running Kafka cluster with  a broker listening on localhost:9092 before running this code.     Sure! Here is an example of a simple Java program that produces messages into Kafka:     import org.apache.kafka.clients.producer.KafkaProducer;  import org.apache.kafka.clients.producer.ProducerRecord; import java.util.Properties;  public class SimpleKafkaProducer  public static void main(String args)  Properties props = new Properties(); props.put("bootstrap.servers", "localhost:9092"); props.put("acks", "all"); props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");  try (KafkaProducer producer = new KafkaProducer<>(props))  for (int i = 0; i <10; i++)   ProducerRecord record = new ProducerRecord<>("my-topic", Integer.toString(i), "Message " + i); producer.send(record); System.out.println("Sent message: " + record.value());  catch (Exception e)  e.printStackTrace();      This example sends ten messages to a topic called `my-topic`.  Each message has a key which is the string representation of an integer, and a value which is a simple text string. The `producer.send(record)` call asynchronously sends  each record to Kafka.     Notes     Topic Configuration: Ensure that your topic (`my-topic`) exists in Kafka before running this program  or configure your producer to create topics automatically if needed.     Error Handling: The example includes basic error  handling, but you might want to improve it based on the specifics of your production environment, such as adding retries and better logging.    This setup provides a straightforward example of sending data into Kafka  from a Java application. Adjustments may be necessary depending on your specific environment or requirements, especially around security configurations like  SASL or TLS if used in your Kafka cluster.  It_s clear that how the topic or `topic_name`?    In Apache Kafka, topics are fundamental components where messages  are stored and retrieved. A topic is a logical  channel for storing streams of data. When you produce  (send) data to a Kafka cluster, you're essentially publishing it to one or more topics, which can then be consumed by consumer applications.     Naming Topics in Kafka   The name of a topic (`topic_name`) is an identifier that  uniquely identifies the topic within a Kafka cluster. Here are some guidelines and  best practices for naming topics:      Uniqueness: Topic names must be unique within a single Kafka cluster.     Descriptive: Names should be descriptive enough to convey the type  of data or its purpose. This helps developers and operators quickly understand what each topic  is about.   Convention over Configuration: Use consistent naming conventions across your organization to simplify management and usage.  For instance, you might use names that follow a `project_environment_type`  format:   - Example: `payments-prod-transaction`   Avoid Special Characters: Stick to alphanumeric characters (`a-z`, `A-Z`, `0-9`) and hyphens (`-`). Avoid spaces or special characters as they can complicate scripts or  UI usage.   Versioning: If you need backward compatibility, consider versioning  topics. For example:   - Example: `payments-prod-transaction-v1`   Keep Names Short but Descriptive: Longer names are harder to  remember and type. Keep them concise while still  conveying meaning.        3. Naming Convention for Kafka Topics    a) General Rule  Kafka topic names should follow these guidelines:      Use lower-case letters only.   Separate words with hyphens (-) (e.g., `user-registration-events`).     Avoid spaces, underscores, or special characters.    Be consistent across the organization.     b) Naming Patterns    Purpose Topic Name Pattern   | Data ingestion | `--ingest` (e.g., `sales-order-ingest`) | | Business events | `-` (e.g., `order-service-order-created`) | | Aggregated data | `-` (e.g., `customer-profile-agg`) | | Error or dead-letter topics | `-dlq` (e.g., `user-registration-dlq`) |     c) Example Topics    Topic Description    `payment-service-payment-completed` Emits when a payment is  successfully processed.    `inventory-warehouse-stock-updates` Periodic stock levels from warehouses.    `customer-profile-agg` Aggregated customer  profile data used by downstream services.    ---     5. Data Processing Pipelines   The pipelines transform raw data into enriched, consistent formats for downstream consumers.      5.1 ETL/ELT Stages    Stage Functionality Output    Extract Read from source systems (APIs, databases). Raw JSON or CSV records.    Transform Cleanse, enrich, aggregate. Structured, validated data.    Load Persist into target store. Immutable event logs or materialized views.     5.2 Transformation Logic       Field Normalization: Convert `date_of_birth` to ISO format; standardize currency codes.     Type Casting: Ensure numeric fields are proper integers/floats.    Derived Fields: Compute `age` from `date_of_birth`; calculate total transaction amount  per customer.     5.3 Loading Strategy     Event Store (Append-only): Write each transformed  record as a JSON event with metadata (`event_id`, `timestamp`, `source`). Supports audit trail and replay.   Materialized View: Periodically materialize aggregated data into relational tables for  OLAP queries (e.g., customer profiles, transaction summaries).         4. Data Quality Management    4.1 Validation Rules    Rule Description    `NotNull` Critical fields (`customer_id`, `transaction_id`) must not be  null.    `Unique` Composite key `(source, customer_id)` must be unique per source.    `DateRange` `transaction_date` cannot be in the future or older than 5 years.    `AmountSign` `amount` must be non-zero; negative amounts represent refunds.    `CurrencyCode` Must conform to ISO 4217 standard (e.g., USD, EUR).     4.2 Transformation Functions       Deduplication: If multiple records for the same `(source, customer_id)` exist, retain the one with the most  recent `last_modified_date`.   Conflict Resolution: In case of differing values for a field across sources (e.g., differing `name`), prioritize the source based on a predefined trust hierarchy (`SourceA >SourceB >SourceC`).       4.3 Error Handling    Severity Action    Warning Log error; proceed with partial data if critical fields are present.    Error Abort processing of current record; flag for manual review.    ---     6. Summary of Key Differences and Their Impact    Aspect Source A (ERP) Source B (CRM) Source C (External API) Impact on ETL Pipeline    Primary Key `CustomerID` (numeric, internal) `AccountNumber` (alphanumeric,  external) `api_id` (string, external) Need to generate surrogate keys; join logic varies.    Data Types Numeric, dates, decimals Strings, dates Strings, numbers,  booleans Type casting required per source.    Nullability Some columns allow nulls Many columns mandatory Optional  fields present Handle missing values differently per field.    Data Formats `YYYY-MM-DD` for dates Varies (`MM/DD/YYYY`, etc.) ISO 8601, epoch timestamps Date parsing  functions per source.    Character Set ASCII UTF-8 UTF-8 Ensure consistent encoding in output.    ---     3. Data Validation and Transformation    3.1 Validation Rules   For each field, implement validation checks:     Field Required? Allowed Values / Format Validation Action    `client_id` Yes Non-empty string Reject if null/empty    `project_name` No Any string Strip leading/trailing  spaces    `file_name` Yes Valid filename (no illegal chars) Replace or escape  invalid chars    `data_type` Yes Enumerated (`string`, `integer`, `float`,  `date`, etc.) Map to internal type codes    `data_length` Optional Positive integer (depending on type) Validate range    `required_flag` No `Y`/`N` or boolean Default to `N` if missing    `default_value` No Depends on data_type Cast/validate accordingly     3.2 Handling Missing or Malformed Entries       Mandatory Fields: If a mandatory field is missing (e.g., `data_type`), log an error and skip the record.    Data Type Mismatch: Attempt to coerce values; if coercion fails, log warning and set to null or default.    Range Violations: For numeric fields (`data_length`, etc.), enforce  sensible bounds; if out of range, clip to nearest valid  value.     3.3 Validation Checks     Uniqueness: Verify that the combination of `table_name` and `column_name` is unique.     Foreign Key Constraints: Ensure that referenced tables exist  in the schema catalog.   Consistency with Database Metadata: Cross-check column lengths,  nullability, etc., against database system catalogs.          4. Performance and Robustness Considerations    Aspect Current Implementation (MySQL) Alternative Approaches    Scalability Handles up to ~1_M rows comfortably; performance degrades beyond  10_M due to table size. PostgreSQL with partitioning can scale to >100_M rows while maintaining query performance.    Query Performance Index on `id` suffices for simple scans; complex queries (joins, aggregates) may suffer as table grows. In PostgreSQL, multi-column indexes and partition-wise pruning improve heavy analytic  workloads.    Data Integrity Enforces foreign key constraints via MySQL's  engine. PostgreSQL also supports robust referential integrity; can enforce more sophisticated  rules (triggers).    Scalability Horizontal scaling limited: MySQL does not support sharding natively. PostgreSQL with Citus or Postgres-XL enables horizontal  partitioning across nodes.    Maintenance Overhead Vacuuming, indexing may be manual; large tables require table rebuilds for schema changes.  PostgreSQL's autovacuum and online DDL reduce downtime.    In summary, while MySQL can serve as a viable backend for storing structured  scientific data_providing robust transactional guarantees and ease of use_the inherent scalability constraints (particularly with respect to horizontal partitioning) may limit its effectiveness in large_scale astrophysical  analyses involving terabyte_level datasets or highly concurrent analytical workloads. PostgreSQL, especially when combined with extensions that support distributed execution, offers a more scalable alternative at the cost  of potentially increased operational complexity.     ---     3. Alternative Data Storage Solutions for Large-Scale Astrophysical Analyses   While relational databases provide a well-understood paradigm  for structured data management, astrophysical applications  often involve large, multidimensional datasets (e.g.,  spectra, images, simulation outputs) that may benefit from specialized storage architectures. Two promising alternatives are columnar storage systems and  distributed file systems.     3.1 Columnar Storage Systems    3.1.1 Overview   Columnar databases store data by columns rather than rows.  Each column is stored contiguously, enabling efficient compression and high read performance for analytical workloads that typically access a  subset of columns. Examples include Apache Parquet, ORC, and column-oriented database engines such as ClickHouse  or Apache Kudu.     3.1.2 Benefits for Astronomical Data     Compression: Homogeneous data types per column allow aggressive compression (e.g., dictionary  encoding), which is beneficial for large numeric arrays  like spectral data.   Column Pruning: Analytical queries often target specific measurements (e.g., fluxes at certain wavelengths). Column pruning reduces I/O by only reading necessary columns.     Predicate Pushdown: Filters on column values can be  applied during read, reducing the amount of data transferred.       3.1.3 Limitations     Complex Data Structures: Nested or variable-length arrays (e.g., irregularly  sampled spectra) are harder to represent efficiently.    Update Overheads: In-place updates may require rewriting entire columns if they depend on many rows.    Schema Rigidity: Adding new measurement types can necessitate schema  evolution, which might be costly in large datasets.         4. Hybrid Storage Strategy   Recognizing that no single storage model perfectly satisfies all requirements, we propose a hybrid approach combining  the strengths of multiple paradigms:     Component Model Purpose Advantages    Raw data store Columnar (e.g., Parquet) Persistent storage of measurement tables Compression, efficient column scans    In-memory cache Key_Value (e.g., Redis, Aerospike) Hot data for fast read/write  O(1) access, low latency    Query layer Relational (Spark SQL / Hive) Complex analytical queries  SQL interface, joins, aggregations    Metadata & lineage Graph (Neo4j) Tracking provenance, dependencies Flexible schema, traversal  queries        Data flow: New measurements are written to the columnar store; a change_feed  or CDC pipeline updates the in_memory cache. Analytical jobs read  from the columnar store via Spark SQL; provenance queries traverse the graph.         4. Practical Implementation Blueprint    Component Technology Rationale Example    Data ingestion Apache Kafka (or Pulsar) Decouples producers/consumers, supports high throughput  and replay Sensors push JSON payloads to a topic    Change_data capture Debezium + Kafka Connect Emits change events  from relational DB or file system changes Captures inserts/updates in the metadata tables    In_memory cache Redis (with Lua scripting) or Hazelcast IMDG Low_latency key/value store  with persistence options Store `asset_id -> asset_type`  mapping    Metadata storage PostgreSQL / MySQL (relational) or MongoDB / Cassandra (NoSQL) Structured, ACID compliant data  Store asset definitions and relationships    Data processing pipeline Apache Flink / Spark Streaming Stateful stream processing with  windowing & joins Compute derived metrics (e.g., energy per unit)    Orchestration Airflow / Prefect DAG scheduling for batch jobs Run daily updates, ETL pipelines    ---     4. Detailed Design    4.1 Schema Overview   Below is a simplified relational schema (other tables omitted for  brevity). If you choose NoSQL, these would translate into collections or column families.      Table Columns Constraints    `assets` `asset_id PK`, `name`, `type`, `installation_date`, `location_id FK` -    `locations` `location_id PK`, `address`, `latitude`, `longitude`  -    `readings` `reading_id PK`, `asset_id FK`, `timestamp`,  `value`, `unit` -    `users` `user_id PK`, `username`, `password_hash`, `role` -    `notifications` `notification_id PK`, `user_id FK`, `asset_id FK`, `message`, `sent_at`,  `read_at` -    ---     5. Security Measures    1. Secure Password Storage    Store passwords using bcrypt or Argon2 hashing with a unique salt per password.      2. Token-Based Authentication    Use JWTs for stateless authentication.   Set short expiration times for access tokens (e.g., 15 minutes).    Refresh tokens are long-lived and stored securely on the client side.      3. HTTPS Everywhere    Enforce HTTPS to secure data in transit.     4. Input Validation & Sanitization    Validate all inputs server-side against a whitelist.     Use parameterized queries or ORM to prevent SQL injection.     5. Rate Limiting    Apply rate limits on authentication endpoints (e.g., max 10 login attempts per hour).      6. Secure Password Storage    Hash passwords with Argon2id:   - `argon2i` is the original version; `argon2id` is hybrid.  - Use a high memory cost, e.g., 64 MB, and moderate time factor.      7. Session Management (If Using Sessions)    Store session IDs in HttpOnly, Secure cookies.    Invalidate sessions after logout or inactivity.     8. Auditing    Log authentication attempts with timestamps and IPs (but avoid logging passwords).         3. Handling a Broken Authentication System   Suppose the authentication system is currently broken: users cannot log in, session tokens  are invalid, etc. We need to identify root causes and propose fixes.      3.1 Common Causes of Authentication Failure    Symptom Possible Cause    Users cannot log in Incorrect password hashing or verification logic; DB  connection issues; mismatched salts/keys    Session token always invalid Token generation uses wrong secret; token expiration too short;  server clock skew; token not stored correctly on client    Token accepted by backend but user denied access Backend checks for  token signature but fails to verify claims; missing roles in JWT payload    Password reset flow broken Email delivery failure;  reset link URL malformed; invalid or expired tokens     3.2 Diagnostic Steps       Check Server Logs: Look for authentication errors, token verification failures.    Verify DB Schema: Ensure password hash and salt columns match expected lengths.     Test Token Generation:   - Use a known secret to generate a JWT manually.  - Decode the token using `jwt.io` or a library to  confirm payload fields.     Validate Token Verification Code:   - Verify that the same secret is used for signing and verification. - Confirm algorithm matches (`HS256`, `RS256`, etc.).      Test Password Hashing Flow:   - Generate hash with known password and compare  against stored hash. - Use the library_s verify function to ensure consistency.     ---     4. Common Pitfalls & Mitigations    Issue Likely Cause Fix    Token fails to decode (e.g., `jwt.DecodeError`) Wrong secret/key or wrong algorithm specified  Ensure both signing and verification use same key/algorithm    Password never matches Using different salts, iterations, or hash functions between signup & login Use a deterministic salt (e.g., derived from  username) or store the generated salt with the user    Password length >20 but still accepted No explicit max length enforced in DB or application layer Add  `CHECK` constraint or validate in code    Token expires immediately Expiration timestamp set too low Set appropriate TTL (e.g., 24h)    User cannot log in after password change Session token not  invalidated Invalidate old tokens or use short-lived access  tokens    ---     Quick Code Snippet (Python with `bcrypt`)    import bcrypt, json   sign up  password = 'mysecurepassword'.encode() hashed_pw = bcrypt.hashpw(password, bcrypt.gensalt()) store_in_db(user_id, hashed_pw)  DB column: bytea or varbinary    log in  stored_hash = fetch_from_db(user_id) if bcrypt.checkpw(password, stored_hash):  generate token (JWT)  payload = 'sub': user_id token = jwt.encode(payload, secret_key, algorithm='HS256') else: raise Exception('Invalid credentials')  print(json.dumps('token': token))   This snippet uses bcrypt for hashing and JWT for tokens. The hash is stored as a binary string; the token is  JSON_encoded.    ---     5. Common Pitfalls & Best Practices    Issue Explanation Remedy    Storing password hashes as text (e.g., base64) May lead to accidental truncation or padding issues.  Store as binary (`varbinary`) if the DB supports it; otherwise, use a fixed_size hex string.    Using weak hashing algorithms MD5/SHA1 are fast and vulnerable to brute force. Use bcrypt, Argon2, scrypt.    Hard_coding salts or pepper values If compromised, all passwords  become vulnerable. Keep them in secure key management services; rotate regularly.    Exposing tokens in logs or URLs Logs may inadvertently record sensitive data. Mask or redact tokens before logging; use POST body instead of query parameters.    Neglecting expiration for tokens Tokens that never expire pose a security risk if leaked. Implement short lifetimes and optional refresh mechanisms.    ---     5. Summary & Best_Practice Checklist     Never store plaintext passwords.   Use per_user salts + PBKDF2/Argon2 with a high iteration count  (__10_).   Store only the salt, hash, and optional cost factor in the database.    For authentication tokens: use a random, cryptographically strong string; keep them server_side and enforce expiration / revocation.   Keep all secrets out of source code (environment variables  or secret management tools).    Task Recommended Action    Password hashing PBKDF2/HMAC_SHA256 with 100,000+ iterations; store salt + hash.    Token generation SecureRandom.base64(32) or UUIDv4; keep in DB; set expiration (e.g., 30_days).    Secret storage Environment variables / Vault; never commit secrets.    Auditing Log token issuance and revocation events.     By following these guidelines, you_ll ensure that user credentials and authentication tokens are  handled securely, protecting both your users and your application from common security  threats.

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