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To empower startups with comprehensive data solutions, enabling them to unlock, insights and optimize operations.
Data Governance
Setup Data Governance Like Big Tech
Data
Data Lineages
Build metrics packs and data catalogs for better insights. like Big Tech.
What we offer?
01
Data Strategy & Consulting
02
Data Storage & Management
03
Data Ingestion & Integration
04
Data Processing & Analytics
05
Business Intelligence (BI)
06
Data Governance & Versioning
1. Data Storage and Management:
- Databases:
- SQL Databases: MySQL, PostgreSQL, SQL Server, Oracle
- NoSQL Databases: MongoDB, Cassandra, DynamoDB, Couchbase
- Columnar Databases: Google BigQuery, Amazon Redshift, Apache HBase
- Graph Databases: Neo4j, Amazon Neptune
- Data Warehouses:
- Snowflake
- Google BigQuery
- Amazon Redshift
- Azure Synapse Analytics
- Data Lakes:
- Amazon S3
- Azure Data Lake
- Google Cloud Storage (GCS)
- Apache Hadoop Distributed File System (HDFS)
2. Data Processing Frameworks:
- Batch Processing:
- Apache Spark
- Apache Hadoop (MapReduce, Hive)
- Trino (Presto)
- AWS Glue
- Google Dataflow
- Stream Processing:
- Apache Kafka
- Apache Flink
- Apache Beam
- Apache Storm
- Confluent Kafka
- AWS Kinesis
3. Data Ingestion:
- ETL/ELT Tools:
- Apache Nifi
- Talend
- Airbyte
- Fivetran
- Stitch
- Informatica
- Messaging Systems:
- Apache Kafka
- RabbitMQ
- Google Pub/Sub
- Amazon SQS/SNS
- API Integration:
- REST APIs
- GraphQL
4. Data Orchestration and Workflow Management:
- Scheduling and Orchestration Tools:
- Apache Airflow
- Prefect
- Dagster
- Luigi
- AWS Step Functions
- Azure Data Factory (ADF)
5. Cloud Platforms and Services:
Data Lake, Synapse Analytics, CosmosDB, Data Factory, Event Hubs, Functions
AWS:
S3, RDS, Redshift, Glue, EMR, Athena, Lambda, Kinesis
Google Cloud:
GCS, BigQuery, Dataflow, Pub/Sub, Cloud Functions, Dataproc
Azure:
6. Data Transformation and Analytics:
- SQL Engines:
- Apache Hive
- Apache Impala
- AWS Athena
- Databricks SQL
- Data Transformation Tools:
- dbt (Data Build Tool)
- Matillion
- Azure Databricks
7. Data Versioning and Governance:
- Data Lineage and Governance:
- Apache Atlas
- Amundsen
- Collibra
- DataHub
- Data Catalogs:
- AWS Glue Data Catalog
- Google Data Catalog
- Alation
8. Data Visualization and Business Intelligence (BI) Tools:
- BI Tools:
- Tableau
- Power BI
- Looker
- Metabase
- Superset
- Qlik
9. DevOps and Infrastructure as Code (IAC):
- Version Control:
- Git (GitHub, GitLab, Bitbucket)
- CI/CD Tools:
- Jenkins, CircleCI, GitLab CI
- Containerization and Orchestration:
- Docker, Kubernetes, AWS EKS, GKE, AKS
- Infrastructure as Code:
- Terraform, CloudFormation, Pulumi
10. Programming Languages:
- Languages for Data Engineering:
- Python
- Java
- Scala
- SQL
- Go
- R (for analytics)
11. Machine Learning/Data Science Integration (For Data Engineers supporting ML/AI):
- ML Platforms:
- Databricks
- AWS SageMaker
- Google Vertex AI
This stack can vary depending on the specific requirements of the project, company, or industry. A typical data engineering role often focuses on a subset of these technologies.
Testimonials
★ ★ ★ ★ ★
Average Rating: 4.5 (Based on 100+ Reviews)
Excellent
Mishap’s data governance and data quality solutions have greatly improved our data management processes.
★ ★ ★ ★ ★
Sarah Williams
Outstanding Support
The A/B testing data provided by Mishap has helped us optimize our marketing campaigns and improve conversion
★ ★ ★ ★ ★
John Doe
Great Service
I have been using Mishap’s data infrastructure services for my startup and I am extremely satisfied with the scalability and performance.
★ ★ ★ ★ ★
Jane Smith
Highly Recommended
Mishap’s streaming pipelines and real-time analytics have been instrumental in helping us make data-driven decisions.
★ ★ ★ ★ ★
David Johnson
Scalable Data