Full automation of data pipelines allows organizations to extract data at its source, transform it, integrate it with other sources and fuel business applications and data analytics. It’s an important brick of a truly data driven ecosystem.
Data Pipeline Automation services we perform
- Design of the end-to-end data flow architecture
- Implementation of cloud based ETL processes
- Integration with existing data sources and services
- Design and development of the data driven applications
Benefits of Data Pipeline Automation
- Enabler for a real time data-driven decision making
- Better data analytics and business insights
- Identification and utilization of dark data
- Scalable and easy to maintain cloud solutions
Our Clients
Here are some of our clients that trust us with their online solutions
Our Technology Tool Stack
Cloud Toolset
- Analytical Databases: Big Query, Redshift, Synapse
- ETL: Databricks, DataFlow, DataPrep, Fivetran, Matillion, Streamsets and Hevo
- Scalable Compute Engines: GKE, AKS, EC2, DataProc
- Process Orchestration: AirFlow / Composer, Bat
- Platform Deployment & Scaling: terraform, custom tools
Open Source
- Support for all Hadoop distributions: Cloudera, Hortonworks, MapR
- Hadoop tools: hdfs, hive, pig, spark, flink
- No SQL Databases: Cassandra MongoDB, Hbase, Phoenix
- Process Automation: oozie, Airflow
<span data-metadata=""><span data-buffer="">Visualisation Tools
- Power BI
- Tableau
- Data Studio
- D3.js
- Qlikview
- QlikSense
- Cognos
<span data-metadata=""><span data-buffer="">Programming Skills
- Python: numpy, pandas, matplotlib, scikit-learn, scipy, spark, pyspark & more
- Scala, Java
- SQL, T-SQL, H-SQL, PL/SQL
Data platforms
- Snowflake
- Azure Synapse
- Databricks
- Bigquery
- Salesforce
- Sap
- Teradata
Data Engineering FAQ
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1. What is data engineering?
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2. What skills are required for a data engineer?
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3. What tools and technologies are commonly used in data engineering?
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4. What are some common data engineering challenges?
Data engineering is the process of designing, building, and maintaining the infrastructure required to store, process, and analyze large volumes of data. Data engineers are responsible for developing data pipelines, data warehouses, data lakes, and other data-related infrastructure to support data-driven decision-making.
Data engineers should have a strong background in computer science, mathematics, and statistics. They should also have experience with programming languages such as Python, Java, and SQL, as well as with data warehousing, data lakes, and data integration tools. Strong communication and collaboration skills are also important, as data engineers often work closely with data scientists, business analysts, and other stakeholders.
Common tools and technologies used in data engineering include Apache Spark, Apache Kafka, Apache Hadoop, Apache Hive, Apache Airflow, Apache Beam, and Apache NiFi. These tools and technologies are used to build data pipelines, data warehouses, data lakes, and other data-related infrastructure.
Some common data engineering challenges include data quality issues, data integration challenges, data security and privacy concerns, and data governance challenges. Data engineers must work to address these challenges and ensure that data is accurate, consistent, secure, and accessible to the right people at the right time.
