Data Platform
with superpowers
Data Platform
with superpowers
Transform data management and AI with our cutting-edge platform. Say goodbye to manual tasks, embrace streamlined workflows, and unlock insights for informed decisions, all in a simplified, efficient way.
Made Easy
Ingest
Seamlessly integrate your data from multiple sources with Datazone's expansive offering of over +600 connectors. Streamline your data ingestion process, enabling your team to focus more on insights and less on gathering.
Connect Any Data Source
Seamlessly connect and ingest your data with just a few clicks
Connect Any Data Source
Seamlessly connect and ingest your data with just a few clicks
Connect Any Data Source
Seamlessly connect and ingest your data with just a few clicks
Connect Any Data Source
Seamlessly connect and ingest your data with just a few clicks
Connect Any Data Source
Seamlessly connect and ingest your data with just a few clicks
Transform Data
Develop
Leverage the power of Apache Spark, the industry-leading data processing engine, to effortlessly transform your data. With Datazone, you can turn raw data into valuable insights, powering your business decisions.
Build & Transform Data Pipelines Automatically
Datazone unifies your entire data pipeline journey. Ingest, transform, and automate with confidence - all in one powerful platform. Build and deploy end-to-end data pipelines in minutes, not months. No more context switching, just seamless data engineering.
PySpark
Python
SQL Query
Quick Integration in Minutes
Simplify your data source connections. Extract and configure with our intuitive interface. Connect multiple sources instantly, without complex configurations. Cut setup time from days to minutes.
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from datazone import Extract
orders_mysql_extract = Extract(
source_id="prod-mysql-db",
query="""
SELECT
order_id,
order_date,
customer_id,
product_id,
quantity,
unit_price
FROM orders
WHERE active = 1;
""",
output_dataset_name="orders",
)
Quick Integration in Minutes
Simplify your data source connections. Extract and configure with our intuitive interface. Connect multiple sources instantly, without complex configurations. Cut setup time from days to minutes.
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from datazone import Extract
orders_mysql_extract = Extract(
source_id="prod-mysql-db",
query="""
SELECT
order_id,
order_date,
customer_id,
product_id,
quantity,
unit_price
FROM orders
WHERE active = 1;
""",
output_dataset_name="orders",
)
Quick Integration in Minutes
Simplify your data source connections. Extract and configure with our intuitive interface. Connect multiple sources instantly, without complex configurations. Cut setup time from days to minutes.
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from datazone import Extract
orders_mysql_extract = Extract(
source_id="prod-mysql-db",
query="""
SELECT
order_id,
order_date,
customer_id,
product_id,
quantity,
unit_price
FROM orders
WHERE active = 1;
""",
output_dataset_name="orders",
)
Quick Integration in Minutes
Simplify your data source connections. Extract and configure with our intuitive interface. Connect multiple sources instantly, without complex configurations. Cut setup time from days to minutes.
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from datazone import Extract
orders_mysql_extract = Extract(
source_id="prod-mysql-db",
query="""
SELECT
order_id,
order_date,
customer_id,
product_id,
quantity,
unit_price
FROM orders
WHERE active = 1;
""",
output_dataset_name="orders",
)
Quick Integration in Minutes
Simplify your data source connections. Extract and configure with our intuitive interface. Connect multiple sources instantly, without complex configurations. Cut setup time from days to minutes.
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from datazone import Extract
orders_mysql_extract = Extract(
source_id="prod-mysql-db",
query="""
SELECT
order_id,
order_date,
customer_id,
product_id,
quantity,
unit_price
FROM orders
WHERE active = 1;
""",
output_dataset_name="orders",
)
Data Branching
Manage your data like code, experiment fearlessly and ensure only successful transformations make it to production branch.
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Schedule Your Data Pipelines
Automate your pipeline schedules with flexible intervals. Monitor and manage transformations in real-time. Ensure timely data delivery for business operations.
Notebook Environment
Data science workspace with interactive notebooks . Transform data, perform exploratory analysis, and share insights with your team in real-time
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build & Transform Data Pipelines Automatically
Datazone unifies your entire data pipeline journey. Ingest, transform, and automate with confidence - all in one powerful platform. Build and deploy end-to-end data pipelines in minutes, not months. No more context switching, just seamless data engineering.
PySpark
Python
SQL Query
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Transform with Ease
Define transformations using simple Python functions. Process your data efficiently with PySpark SQL integration.
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from datazone import Dataset, transform, Input
from pyspark.sql import functions as F
@transform(
input_mapping={"retail_data": Input(Dataset("retail-data"))},
materialized=True,
)
def report_data(retail_data):
transformed_df = (
retail_data.dropDuplicates()
.withColumn("total_amount", F.col("quantity") * F.col("unit_price"))
.groupBy("order_date")
.agg(
F.count("order_id").alias("total_orders"),
F.sum("total_amount").alias("daily_revenue"),
F.avg("total_amount").alias("avg_order_value"),
)
.orderBy("order_date")
)
return transformed_df
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build & Transform Data Pipelines Automatically
Datazone unifies your entire data pipeline journey. Ingest, transform, and automate with confidence - all in one powerful platform. Build and deploy end-to-end data pipelines in minutes, not months. No more context switching, just seamless data engineering.
PySpark
Python
SQL Query
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build Your Own API
Create custom API endpoints with Python simplicity. Configure rate limits, pagination and filters for your data services.
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from datazone import Endpoint, Dataset
retail_data_endpoint = Endpoint(
name="retail-api",
source=Dataset("raw-retail-data"),
config={
"rate_limit": {"requests_per_minute": 100, "burst": 20},
"default_page_size": 20,
"filterable_columns": ["order_id", "order_date"]
},
path="/retail-data",
)
Build & Transform Data Pipelines Automatically
Datazone unifies your entire data pipeline journey. Ingest, transform, and automate with confidence - all in one powerful platform. Build and deploy end-to-end data pipelines in minutes, not months. No more context switching, just seamless data engineering.
PySpark
Python
SQL Query
Your Way
Serve
Make your data accessible and usable. With Datazone, serve data directly from our platform to your Business Intelligence tools via ODBC/JDBC or provide API access for 3rd party applications. Simplify data delivery, and make it work for you.
Query with SQL
Query billions of rows with sub-second performance. Manage complex analytical workloads with integrated ClickHouse engine.
Real-Time Data Streaming
Stream and process your data in real-time. Monitor live data flows and react instantly to changes
SQL Interfaces
Connect through familiar MySQL and PostgreSQL interfaces. Query your
data using standard SQL syntax and tools.
Connect through familiar MySQL and PostgreSQL interfaces. Query your data using standard SQL syntax and tools.
Multi-Protocol Data Access
Access your data through multiple protocols including JDBC, ODBC, and REST APIs. Enable easy integration with business intelligence tools, applications, and AI services.
Query with Integrated ClickHouse
Query billions of rows with sub-second performance. Manage complex analytical workloads with integrated ClickHouse engine.
Contact us
Ready to Elevate Your Experience? Get in Touch!
Contact us
Ready to Elevate Your Experience? Get in Touch!
Contact us
Ready to Elevate Your Experience? Get in Touch!
Contact us
Ready to Elevate Your Experience? Get in Touch!
Datazone
Simplified Data & AI Platform for Enhanced Productivity and Efficiency
Datazone
Simplified Data & AI Platform for Enhanced Productivity and Efficiency
Datazone
Simplified Data & AI Platform for Enhanced Productivity and Efficiency