Data Platform Evolution in 2022: Trends and Technologies

The data platform landscape shifted significantly in 2022. Let’s examine the key trends, technologies, and architectural patterns that defined the year.

The Lakehouse Wins

The convergence of data lakes and data warehouses became mainstream:

# Delta Lake enables lakehouse architecture
from delta import DeltaTable
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
    .getOrCreate()

# ACID transactions on data lake
df = spark.read.parquet("abfss://raw@lake.dfs.core.windows.net/sales/")

# Write as Delta table
df.write.format("delta") \
    .mode("overwrite") \
    .save("abfss://curated@lake.dfs.core.windows.net/sales/")

# Time travel queries
DeltaTable.forPath(spark, "abfss://curated@lake.dfs.core.windows.net/sales/") \
    .history() \
    .show()

# Schema enforcement
df.write.format("delta") \
    .option("mergeSchema", "true") \
    .mode("append") \
    .save("abfss://curated@lake.dfs.core.windows.net/sales/")

Real-Time Analytics Growth

Streaming data processing became more accessible:

# Azure Event Hubs + Spark Streaming
from pyspark.sql.functions import from_json, col
from pyspark.sql.types import StructType, StringType, DoubleType, TimestampType

# Define schema for IoT events
schema = StructType() \
    .add("device_id", StringType()) \
    .add("temperature", DoubleType()) \
    .add("humidity", DoubleType()) \
    .add("timestamp", TimestampType())

# Read from Event Hubs
df = spark.readStream \
    .format("eventhubs") \
    .option("eventhubs.connectionString", connection_string) \
    .load()

# Parse and process
events = df.select(
    from_json(col("body").cast("string"), schema).alias("event")
).select("event.*")

# Real-time aggregations
aggregated = events \
    .withWatermark("timestamp", "10 minutes") \
    .groupBy(
        window("timestamp", "5 minutes"),
        "device_id"
    ).agg(
        avg("temperature").alias("avg_temp"),
        max("temperature").alias("max_temp")
    )

# Write to Delta Lake
aggregated.writeStream \
    .format("delta") \
    .outputMode("append") \
    .option("checkpointLocation", "/checkpoints/iot") \
    .start("abfss://curated@lake.dfs.core.windows.net/iot_aggregates/")

Data Mesh Architecture

Organizations adopted data mesh principles:

# Data product definition
data_product:
  name: Customer 360
  domain: Customer Experience
  owner: customer-success-team@company.com

  description: |
    Unified view of customer interactions across all touchpoints.
    Updated every 15 minutes from source systems.

  schema:
    - name: customer_id
      type: string
      description: Unique customer identifier
    - name: lifetime_value
      type: decimal(18,2)
      description: Total revenue from customer
    - name: churn_score
      type: float
      description: ML-predicted churn probability

  quality:
    freshness: 15 minutes
    completeness: 99.5%
    accuracy: Customer ID must exist in CRM

  access:
    - team: marketing
      level: read
    - team: sales
      level: read
    - team: analytics
      level: read_write

  lineage:
    sources:
      - crm.customers
      - ecommerce.orders
      - support.tickets
    transformations:
      - deduplicate_customers
      - calculate_ltv
      - predict_churn

DataOps Maturation

CI/CD for data pipelines became standard:

# azure-pipelines.yml for data platform
trigger:
  branches:
    include:
      - main
  paths:
    include:
      - pipelines/**
      - transformations/**

stages:
  - stage: Validate
    jobs:
      - job: SchemaValidation
        steps:
          - script: |
              python -m great_expectations checkpoint run data_quality
            displayName: 'Run data quality checks'

      - job: SQLValidation
        steps:
          - script: |
              sqlfluff lint transformations/**/*.sql
            displayName: 'Lint SQL files'

  - stage: Test
    dependsOn: Validate
    jobs:
      - job: UnitTests
        steps:
          - script: |
              pytest tests/unit -v --cov=transformations
            displayName: 'Run unit tests'

      - job: IntegrationTests
        steps:
          - script: |
              pytest tests/integration -v --env=dev
            displayName: 'Run integration tests'

  - stage: DeployDev
    dependsOn: Test
    jobs:
      - deployment: DeployPipelines
        environment: 'data-platform-dev'
        strategy:
          runOnce:
            deploy:
              steps:
                - task: AzureCLI@2
                  inputs:
                    azureSubscription: 'data-platform-dev'
                    scriptType: 'bash'
                    scriptLocation: 'inlineScript'
                    inlineScript: |
                      az synapse pipeline create \
                        --workspace-name synapse-dev \
                        --name ETL-Pipeline \
                        --file @pipelines/etl.json

Data Quality as Code

Programmatic data quality became essential:

import great_expectations as gx

# Create expectation suite
context = gx.get_context()

suite = context.create_expectation_suite("customer_data_quality")

# Define expectations
expectations = [
    # Completeness
    {
        "expectation_type": "expect_column_values_to_not_be_null",
        "kwargs": {"column": "customer_id"}
    },
    # Uniqueness
    {
        "expectation_type": "expect_column_values_to_be_unique",
        "kwargs": {"column": "customer_id"}
    },
    # Format
    {
        "expectation_type": "expect_column_values_to_match_regex",
        "kwargs": {
            "column": "email",
            "regex": r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
        }
    },
    # Range
    {
        "expectation_type": "expect_column_values_to_be_between",
        "kwargs": {
            "column": "age",
            "min_value": 0,
            "max_value": 150
        }
    },
    # Referential integrity
    {
        "expectation_type": "expect_column_values_to_be_in_set",
        "kwargs": {
            "column": "country_code",
            "value_set": ["US", "CA", "UK", "AU", "DE", "FR"]
        }
    },
    # Freshness
    {
        "expectation_type": "expect_column_max_to_be_between",
        "kwargs": {
            "column": "updated_at",
            "min_value": {"$PARAMETER": "yesterday"},
            "max_value": {"$PARAMETER": "now"}
        }
    }
]

for exp in expectations:
    suite.add_expectation(gx.expectations.ExpectationConfiguration(**exp))

context.save_expectation_suite(suite)

Semantic Layer Adoption

Business-friendly data access grew:

# dbt semantic layer definition
semantic_models:
  - name: orders
    defaults:
      agg_time_dimension: order_date
    description: Order transactions

    entities:
      - name: order
        type: primary
        expr: order_id

      - name: customer
        type: foreign
        expr: customer_id

    dimensions:
      - name: order_date
        type: time
        type_params:
          time_granularity: day
        expr: created_at

      - name: order_status
        type: categorical
        expr: status

      - name: region
        type: categorical
        expr: shipping_region

    measures:
      - name: order_total
        description: Total order value
        agg: sum
        expr: total_amount

      - name: order_count
        description: Number of orders
        agg: count
        expr: order_id

      - name: average_order_value
        description: Average order value
        agg: average
        expr: total_amount

metrics:
  - name: revenue
    description: Total revenue
    type: simple
    type_params:
      measure: order_total

  - name: orders_per_customer
    description: Average orders per customer
    type: derived
    type_params:
      expr: order_count / count(distinct customer)

Key Lessons from 2022

data_platform_lessons = {
    "governance_early": "Implement data governance from day one, not as an afterthought",
    "quality_automated": "Data quality checks should run automatically in pipelines",
    "self_service": "Enable self-service while maintaining governance guardrails",
    "real_time_matters": "Batch-only is no longer sufficient for most use cases",
    "cloud_native": "Cloud-native services reduce operational burden significantly",
    "ai_integration": "AI/ML is a first-class citizen in the data platform"
}

Conclusion

2022 marked the maturation of modern data platforms. The lakehouse architecture proved its value, real-time capabilities became accessible, and DataOps practices solidified. The trends point toward continued consolidation and simplification of the data platform landscape in the years ahead.

Resources

• Delta Lake
• dbt
• Great Expectations
• Azure Synapse Analytics